Vehicle sensor calibration system using dynamic scene

ABSTRACT

Sensors coupled to a vehicle are calibrated using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. The sensors capture data at each vehicle orientation along the rotation. The vehicle&#39;s computer identifies representations of the sensor targets within the data captured by the sensors, and calibrates the sensor based on these representations, for example by generating a transformation based on mapping positions of these representations in the data to known positions of the sensor targets, such as to correct lens distortion, the transformation applied to post-calibration sensor data.

TECHNICAL FIELD

The present invention generally pertains to calibration of sensors that are used by vehicles. More specifically, the present invention pertains to use of a dynamic scene to perform intrinsic and extrinsic calibrations of various sensors, such as cameras and range sensors, that are coupled to a vehicle and used by the vehicle to identify its surroundings.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can navigate without a human driver. An exemplary autonomous vehicle includes a plurality of sensor systems, such as, but not limited to, a camera sensor system, a light detection and ranging (LIDAR) sensor system, or a radio detection and ranging (RADAR) sensor system, amongst others, wherein the autonomous vehicle operates based upon sensor signals output by the sensor systems. Specifically, the sensor signals are provided to an internal computing system in communication with the plurality of sensor systems, wherein a processor executes instructions based upon the sensor signals to control a mechanical system of the autonomous vehicle, such as a vehicle propulsion system, a braking system, or a steering system. Such sensors may also be mounted on other vehicles, such as vehicles that are used to generate or update street maps as they drive.

A wide range of manufacturing defects or discrepancies can exist in vehicles, sensors, and mounting hardware that affixes the sensors to the vehicles. Because of these discrepancies, different sensors mounted to different vehicles may capture slightly different data, even when those vehicles are at the exact same position, and even when the vehicles are brand new. For example, a lens of one camera may be warped slightly (or include some other imperfection) compared to a lens of another camera, one vehicle may include a newer hardware revision or version of a particular sensor than another, one vehicle's roof may be a few millimeters higher or lower than another vehicle's roof, or a skewed screw used in a mounting structure for a sensor on one vehicle may tilt the mounting structure slightly. Such imperfections and variations in manufacturing can impact sensor readings and mean that there no two vehicles capture sensor readings in quite the same way, and thus no two vehicles interpret their surroundings via sensor readings in quite the same way. With use, vehicles can drift even further apart in their sensor readings due to exposure to the elements, for example through exposure to heat, rain, dust, frost, rocks, pollution, vehicular collisions, all of which can further damage or otherwise impact a vehicle or its sensor.

Sensors typically capture data and provide results in a standardized manner that does not, by itself, test or account for intrinsic properties of each sensor, such as the position and angle of the sensor or properties of a lens, or for extrinsic relationships between sensors that capture data from similar areas. Because of this, it can be unclear whether a discrepancy in measurements between two vehicles can be attributed to an actual difference in environment or simply different properties of vehicle sensors. Because autonomous vehicles are trusted with human lives, it is imperative that autonomous vehicles have as robust an understanding of their environments as possible, otherwise a vehicle might perform an action that it should not perform, or fail to perform an action that it should perform, either of which can result in a vehicular accident and put human lives at risk. Other sensor-laden vehicles, such as those that collect data for maps or street-level imagery, can produce unreliable maps or images if they cannot account for the properties of their sensors, which can then in turn confuse both human vehicles and autonomous vehicles that rely on those maps, again risking human life.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the present technology will become apparent by reference to specific implementations illustrated in the appended drawings. A person of ordinary skill in the art will understand that these drawings only show some examples of the present technology and would not limit the scope of the present technology to these examples. Furthermore, the skilled artisan will appreciate the principles of the present technology as described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an autonomous vehicle and remote computing system architecture.

FIG. 2A illustrates a camera calibration target with a checkerboard pattern on a planar substrate.

FIG. 2B illustrates a camera calibration target with a ArUco pattern on a planar substrate.

FIG. 2C illustrates a camera calibration target with a crosshair pattern on a planar substrate.

FIG. 2D illustrates a range sensor calibration target with a trihedral shape.

FIG. 2E illustrates a combined range sensor and camera calibration target with apertures from a planar substrate that are surrounded by visually recognizable markings.

FIG. 3 illustrates a top-down view of a hallway calibration environment in which a vehicle traverses a drive path along which the vehicle is flanked by vehicle sensor calibration targets.

FIG. 4 illustrates a perspective view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by vehicle camera calibration targets rotates a vehicle so that the vehicle can perform intrinsic calibration of its camera sensors.

FIG. 5 illustrates a perspective view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by combined vehicle range sensor and vehicle camera calibration targets rotates a vehicle so that the vehicle can perform extrinsic calibration of its range sensors and camera sensors.

FIG. 6 illustrates a top-down view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by both vehicle camera calibration targets and combined vehicle range sensor and vehicle camera calibration targets rotates a vehicle so that the vehicle can perform both intrinsic calibration of its camera sensors and extrinsic calibration of its range sensors and camera sensors.

FIG. 7 illustrates a system architecture of an dynamic scene calibration environment.

FIG. 8A illustrates a top-down view of a turntable with a vehicle guide rail, and a vehicle driving onto the turntable centered relative to the vehicle guide rail.

FIG. 8B illustrates a vehicle having successfully driven onto the turntable centered along the vehicle guide rail, the vehicle thus centered with respect to the turntable.

FIG. 8C illustrates a top-down view of a turntable with a vehicle guide rail, and a vehicle driving onto the turntable off-center relative to the vehicle guide rail.

FIG. 8D illustrates a vehicle having driven partially onto the turntable while off-center relative to the vehicle guide rail, the vehicle guide rail guiding the vehicle's path to center the vehicle with respect to the turntable.

FIG. 9 is a flow diagram illustrating operation of a calibration environment.

FIG. 10 is a flow diagram illustrating operations for intrinsic calibration of a vehicle sensor using a dynamic scene.

FIG. 11 is a flow diagram illustrating operations for extrinsic calibration of two sensors in relation to each other using a dynamic scene.

FIG. 12 is a flow diagram illustrating operations for interactions between the vehicle and the turntable.

FIG. 13 is a flow diagram illustrating operations for detection of, and compensation for, a sloped turntable surface.

FIG. 14A is a flow diagram illustrating operations for interactions between the vehicle and a lighting system.

FIG. 14B is a flow diagram illustrating operations for interactions between the vehicle and a target control system.

FIG. 15 shows an example of a system for implementing certain aspects of the present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the present technology. In some instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by more or fewer components than shown.

The disclosed technologies address a need in the art for improvements to vehicle sensor calibration technologies. Use of a dynamic calibration scene with an automated turntable or carousel system improves the functioning of sensor calibration by improving runtime-efficiency, space-efficiency, comprehensiveness of calibration, and consistency of vehicle sensor calibration. The described vehicle sensor calibration technologies ultimately transform vehicle sensors from an uncalibrated state to a calibrated state. The described vehicle sensor calibration technologies are implemented using a vehicle, the vehicle's sensors, a turntable and other potential components of a dynamic scene, one or more computing devices associated with the other components, each of which is integral at least one embodiment of the vehicle sensor calibration technologies.

Sensors coupled to a vehicle are calibrated using a dynamic scene with sensor targets around a motorized turntable that rotates the vehicle to different orientations. The sensors capture data at each vehicle orientation along the rotation. The vehicle's computer identifies representations of the sensor targets within the data captured by the sensors, and calibrates the sensor based on these representations, for example by generating a transformation based on mapping positions of these representations in the data to known positions of the sensor targets, such as to correct lens distortion, the transformation applied to post-calibration sensor data.

FIG. 1 illustrates an autonomous vehicle and remote computing system architecture.

The autonomous vehicle 102 can navigate about roadways without a human driver based upon sensor signals output by sensor systems 180 of the autonomous vehicle 102. The autonomous vehicle 102 includes a plurality of sensor systems 180 (a first sensor system 104 through an Nth sensor system 106). The sensor systems 180 are of different types and are arranged about the autonomous vehicle 102. For example, the first sensor system 104 may be a camera sensor system and the Nth sensor system 106 may be a Light Detection and Ranging (LIDAR) sensor system. Other exemplary sensor systems include radio detection and ranging (RADAR) sensor systems, Electromagnetic Detection and Ranging (EmDAR) sensor systems, Sound Navigation and Ranging (SONAR) sensor systems, Sound Detection and Ranging (SODAR) sensor systems, Global Navigation Satellite System (GNSS) receiver systems such as Global Positioning System (GPS) receiver systems, accelerometers, gyroscopes, inertial measurement units (IMU), infrared sensor systems, laser rangefinder systems, ultrasonic sensor systems, infrasonic sensor systems, microphones, or a combination thereof. While four sensors 180 are illustrated coupled to the autonomous vehicle 102, it should be understood that more or fewer sensors may be coupled to the autonomous vehicle 102.

The autonomous vehicle 102 further includes several mechanical systems that are used to effectuate appropriate motion of the autonomous vehicle 102. For instance, the mechanical systems can include but are not limited to, a vehicle propulsion system 130, a braking system 132, and a steering system 134. The vehicle propulsion system 130 may include an electric motor, an internal combustion engine, or both. The braking system 132 can include an engine brake, brake pads, actuators, and/or any other suitable componentry that is configured to assist in decelerating the autonomous vehicle 102. In some cases, the braking system 132 may charge a battery of the vehicle through regenerative braking. The steering system 134 includes suitable componentry that is configured to control the direction of movement of the autonomous vehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that can include various lights and signal indicators, parking brake, airbags, etc. The autonomous vehicle 102 further includes a cabin system 138 that can include cabin temperature control systems, in-cabin entertainment systems, etc.

The autonomous vehicle 102 additionally comprises an internal computing system 110 that is in communication with the sensor systems 180 and the systems 130, 132, 134, 136, and 138. The internal computing system includes at least one processor and at least one memory having computer-executable instructions that are executed by the processor. The computer-executable instructions can make up one or more services responsible for controlling the autonomous vehicle 102, communicating with remote computing system 150, receiving inputs from passengers or human co-pilots, logging metrics regarding data collected by sensor systems 180 and human co-pilots, etc.

The internal computing system 110 can include a control service 112 that is configured to control operation of the vehicle propulsion system 130, the braking system 208, the steering system 134, the safety system 136, and the cabin system 138. The control service 112 receives sensor signals from the sensor systems 180 as well communicates with other services of the internal computing system 110 to effectuate operation of the autonomous vehicle 102. In some embodiments, control service 112 may carry out operations in concert one or more other systems of autonomous vehicle 102.

The internal computing system 110 can also include a constraint service 114 to facilitate safe propulsion of the autonomous vehicle 102. The constraint service 116 includes instructions for activating a constraint based on a rule-based restriction upon operation of the autonomous vehicle 102. For example, the constraint may be a restriction upon navigation that is activated in accordance with protocols configured to avoid occupying the same space as other objects, abide by traffic laws, circumvent avoidance areas, etc. In some embodiments, the constraint service can be part of the control service 112.

The internal computing system 110 can also include a communication service 116. The communication service can include both software and hardware elements for transmitting and receiving signals from/to the remote computing system 150. The communication service 116 is configured to transmit information wirelessly over a network, for example, through an antenna array that provides personal cellular (long-term evolution (LTE), 3G, 4G, 5G, etc.) communication.

In some embodiments, one or more services of the internal computing system 110 are configured to send and receive communications to remote computing system 150 for such reasons as reporting data for training and evaluating machine learning algorithms, requesting assistance from remoting computing system or a human operator via remote computing system 150, software service updates, ridesharing pickup and drop off instructions etc.

The internal computing system 110 can also include a latency service 118. The latency service 118 can utilize timestamps on communications to and from the remote computing system 150 to determine if a communication has been received from the remote computing system 150 in time to be useful. For example, when a service of the internal computing system 110 requests feedback from remote computing system 150 on a time-sensitive process, the latency service 118 can determine if a response was timely received from remote computing system 150 as information can quickly become too stale to be actionable. When the latency service 118 determines that a response has not been received within a threshold, the latency service 118 can enable other systems of autonomous vehicle 102 or a passenger to make necessary decisions or to provide the needed feedback.

The internal computing system 110 can also include a user interface service 120 that can communicate with cabin system 138 in order to provide information or receive information to a human co-pilot or human passenger. In some embodiments, a human co-pilot or human passenger may be required to evaluate and override a constraint from constraint service 114, or the human co-pilot or human passenger may wish to provide an instruction to the autonomous vehicle 102 regarding destinations, requested routes, or other requested operations.

The internal computing system 110 can, in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15, or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

As described above, the remote computing system 150 is configured to send/receive a signal from the autonomous vehicle 140 regarding reporting data for training and evaluating machine learning algorithms, requesting assistance from remote computing system 150 or a human operator via the remote computing system 150, software service updates, rideshare pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that is configured to receive data from autonomous vehicle 102 and analyze the data to train or evaluate machine learning algorithms for operating the autonomous vehicle 102. The analysis service 152 can also perform analysis pertaining to data associated with one or more errors or constraints reported by autonomous vehicle 102.

The remote computing system 150 can also include a user interface service 154 configured to present metrics, video, pictures, sounds reported from the autonomous vehicle 102 to an operator of remote computing system 150. User interface service 154 can further receive input instructions from an operator that can be sent to the autonomous vehicle 102.

The remote computing system 150 can also include an instruction service 156 for sending instructions regarding the operation of the autonomous vehicle 102. For example, in response to an output of the analysis service 152 or user interface service 154, instructions service 156 can prepare instructions to one or more services of the autonomous vehicle 102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158 configured to interact with ridesharing applications 170 operating on (potential) passenger computing devices. The rideshare service 158 can receive requests to be picked up or dropped off from passenger ridesharing app 170 and can dispatch autonomous vehicle 102 for the trip. The rideshare service 158 can also act as an intermediary between the ridesharing app 170 and the autonomous vehicle wherein a passenger might provide instructions to the autonomous vehicle to 102 go around an obstacle, change routes, honk the horn, etc.

The rideshare service 158 as depicted in FIG. 1 illustrates a vehicle 102 as a triangle en route from a start point of a trip to an end point of a trip, both of which are illustrated as circular endpoints of a thick line representing a route traveled by the vehicle. The route may be the path of the vehicle from picking up the passenger to dropping off the passenger (or another passenger in the vehicle), or it may be the path of the vehicle from its current location to picking up another passenger.

The remote computing system 150 can, in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15, or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

FIG. 2A illustrates a camera calibration target with a checkerboard pattern on a planar substrate.

The sensor calibration target 220A illustrated in FIG. 2A is a planar board made from a substrate 205, with a pattern 210A printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210A of FIG. 2A is a checkerboard pattern. The substrate 205 may be paper, cardboard, plastic, metal, foam, or some combination thereof. The substrate 205 may in some cases include a translucent or transparent surface upon which the pattern 210A is printed, and which a light source may provide illumination through. The substrate 205 may in some cases include a retroreflective surface upon which the pattern 210A is printed. The retroreflective property of the surface may be inherent to the material of the substrate 205 or may be a separate layer applied to the surface of the substrate, for example by adhering a retroreflective material to the substrate 205 or by painting (e.g., via a brush, roller, or aerosol spray) the substrate 205 with a retroreflective paint. A reflective or retroreflective property may in some cases improve detection using radar, lidar, or other EmDAR sensors. The material and shape of the substrate 205 may also be selected such that the material and/or shape produces a high amount of acoustic resonance or acoustic response to improve detection using SONAR or SODAR sensors. In some cases, the substrate 205, and therefore the target 200A, may be concave, convex, otherwise curved, or some combination thereof. The substrate 205 may in some cases include devices, such as speakers, heat sources, or light sources, that allow improved detection by microphones, infrared sensors, or cameras, respectively.

The sensor calibration target 220A illustrated in FIG. 2A is useful for calibration of a camera of the vehicle, or other sensor that captures visual data. In particular, a camera with a pattern/image/feature recognition system running on computer system 110 can identify the checkerboard pattern 210A of FIG. 2A, and can identify points representing vertices between the dark (black) and light (white) checkers. By drawing lines connecting these points, the camera and computer system 110 can generate a grid. If the camera has a wide-angle lens, such as a fisheye lens or a barrel lens, the resulting grid will be warped so that some checkers will appear curved rather than straight, and so that checkers near the edges of the camera's point of view will appear more squashed, while checkers near the center of the camera's point of view will appear larger and more even. A rectilinear lens provides a similar, is opposite, effect. Based on prior knowledge of what the checkerboard pattern and resulting grid should look like, and its original dimensions, compared against what its representation looks like as captured by the camera, the camera and computing system 110 may identify the effect of the lens and counteract it. The camera and computing system 110 may also identify other parameters of the camera this way, such as position parameters (x, y, z, roll, pitch, yaw), any lens color to be filtered out, any crack or defect in the lens to be filtered out, or a combination thereof.

The sensor calibration target 220A illustrated in FIG. 2A is useful for detection by, and calibration of, a range sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, at least in that the shape of the planar substrate 205 can be detected by the range sensor. For example, flat planar vision targets such as the target 220A can be detected by lidar by relying on planar geometry estimates and using the returned intensity. While FIG. 2A illustrates a square or rectangular substrate 205, the substrate 205 may be circular, semicircular, ellipsoidal, triangular, quadrilateral (trapezoid, parallelogram), pentagonal, hexagonal, heptagonal, octagonal, nonagonal, decagonal, otherwise polygonal, or some combination thereof.

FIG. 2B illustrates a camera calibration target with a ArUco pattern on a planar substrate.

The sensor calibration target 220B illustrated in FIG. 2B, like the sensor calibration target 220A illustrated in FIG. 2A, includes a planar board made from a substrate 205, with a pattern 210B printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210B illustrated in FIG. 2B is an ArUco marker pattern, which includes black border and an inner binary matrix/grid (e.g., each square is dark/black or light/white) which determines its identifier.

By detecting the AuUco pattern, the camera and computing system 110 of the vehicle also identify a grid, similarly to the checkerboard, though potentially with fewer points, as some areas of the ArUco pattern may include contiguous dark/black squares or contiguous light/white squares. By identifying the grid from the representation of the ArUco target captured by the camera (e.g. with lens distortion such as parabolic distortion), and comparing it to a known reference image of the ArUco pattern (e.g., without any distortion), any distortions or other differences may be identified, and appropriate corrections may be generated to counteract these distortions or other differences.

The substrate 205 of FIG. 2B may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a range sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

FIG. 2C illustrates a camera calibration target with a crosshair pattern on a planar substrate.

The sensor calibration target 220C illustrated in FIG. 2B, like the sensor calibration target 220A illustrated in FIG. 2A, includes a planar board made from a substrate 205, with a pattern 210C printed, stamped, engraved, imprinted, or otherwise marked thereon. The pattern 210C illustrated in FIG. 2C is an crosshair marker pattern, which includes four dark/black lines and two dark/black circles centered on a light/white background, and with a gap in the dark/black lines near but not at the center, effectively leaving a “+” symbol in the very center.

The camera and computing system 110 can identify the target 200C by identifying the circles, the lines, and the intersections of the same. In doing so, the crosshair pattern is identified from the representation of the target 220C captured by the camera (e.g. with lens distortion), and can be compared it to a known reference image of the crosshair pattern target 200C (e.g., without any distortion). As with the checkerboard and ArUco targets, any distortions or other differences may be identified, and appropriate corrections may be generated to counteract these distortions or other differences.

The substrate 205 of FIG. 2C may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a range sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

While the only patterns 210A-C discussed with respect to camera sensor targets are checkerboard patterns 210A, ArUco patterns 210B, and crosshair patterns 210C, other patterns that are not depicted can additionally or alternatively be used. For example, bar codes or quick response (QR) codes may be used as patterns 210 that can be recognized using the camera and computing device 110.

FIG. 2D illustrates a range sensor calibration target with a trihedral shape.

The sensor calibration target 220 of FIG. 2D is made to be detected by, and use for calibration of, a range sensor, such as a radar sensor (or LIDAR, SONAR, or SODAR) of the vehicle. In particular, the sensor calibration target 220 of FIG. 2D is trihedral in shape, and may be a concave or convex trihedral corner, essentially a triangular corner of a cube. Alternately, it may be a different shape, such as a corner of a different polyhedron (at least portions of all faces of the polyhedron that touch a particular vertex). Such a shape, especially when concave and where perpendicular faces are included, produces a particularly strong radar echo and thus a particularly strong radar cross section (RCS) because incoming radio waves are backscattered by multiple reflection. The RCS of the trihedral corner target is given by: σ=(4·π·a⁴)/(3·λ²), where a is the length of the side edges of the three triangles, and λ is a wavelength of radar transmitter.

The substrate 205 of the sensor calibration target 220 of FIG. 2D may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner, or include any devices, discussed with respect to the substrate 205 of FIG. 2A. In one embodiment, the substrate 205 of the sensor calibration target 220 of FIG. 2D may be metal, may be electrically conductive, may be reflective or retroreflective, or some combination thereof.

FIG. 2E illustrates a combined range sensor and camera calibration target with apertures from a planar substrate that are surrounded by visually recognizable markings.

The combined range sensor and camera calibration target 250 of FIG. 2E includes multiple apertures 225 in a substrate 205, and includes visual markings or patterns 230 at least partially surrounding each aperture. In particular, the target 250 includes four symmetrical circular or ellipsoid apertures 225 from a light/white substrate 205 with symmetrical dark/black circular or ellipsoid rings 230 around the apertures, with three of the apertures/ring combinations being a first size (e.g., apertures being 30 cm in diameter and the corresponding rings slightly larger) and a fourth aperture/ring combination 240 being a second size (e.g., the aperture being 26 cm in diameter and the corresponding ring slightly larger). The rings around the three larger apertures 225 are likewise larger than the ring around the smaller aperture 240. In some cases, one may be larger than the other three, or two may be larger or smaller than the other two, or some combination thereof. The apertures 225/240 may alternately be referred to as cutouts, holes, voids, orifices, vents, openings, gaps, perforations, interstices, discontinuities or some combination thereof. In some cases, different types of surface discontinuities may be used instead of or in addition to the apertures 225/240, such as raised surfaces or bumps that can also be detected by depth/range sensors such as radar or lidar.

The combined range sensor and camera calibration target 250 of FIG. 2E also includes additional markings or patterns at certain edges of the substrate, identified as target identification (ID) markers 235. The particular combined range sensor and camera calibration target 250 of FIG. 2E includes target identification (ID) markers 235 on the two sides of the substrate opposite the smaller aperture/ring combination 240, but other combined range sensor and camera calibration targets 250 may have one, two, three, or four target identification (ID) markers 235 along any of the four sides of the square substrate, or may have target identification (ID) markers 235 in an amount up to the number of sides of the polygonal substrate 205 where the substrate 205 is shaped like a non-quadrilateral polygon. That is, if the substrate 205 is an octagon, each combined combined range sensor and camera calibration target 250 may have anywhere from zero to eight target identification (ID) markers 235. Different patterns of target identification (ID) markers 235 are further visible in FIG. 5.

The substrate 205 of FIG. 2E may include or be coated with any previously-discussed substrate material and may be warped or shaped in any manner or include any devices discussed with respect to the substrate 205 of FIG. 2A, and therefore may be detected by, and be useful to calibrate a range sensor of the vehicle, such as a LIDAR, SONAR, SODAR, or radar sensor of the vehicle, and may be detected by a microphone or infrared sensor of the vehicle as well.

In some cases, the combined range sensor and camera calibration target 250 may have more or fewer apertures and corresponding visual markings than the four apertures and corresponding visual markings illustrated in FIG. 2E.

Additional targets not depicted in FIG. 2A-2E may also be possible for calibration of different types of vehicle sensors. For example, targets for intrinsic or extrinsic calibration of infrared cameras or other types of infrared sensors of a vehicle 102 may include patterns formed using heating elements, optionally in front of, behind, or beside visual markings or substrate apertures or speakers for extrinsic calibration with cameras or range sensors or microphones, respectively. Targets for intrinsic or extrinsic calibration of microphones or other types of audio sensors of a vehicle 102 may include speakers or patterns formed using speakers, optionally in front of, behind, or beside visual markings or substrate apertures or heating elements for extrinsic calibration with cameras or range sensors or infrared sensors, respectively.

FIG. 3 illustrates a top-down view of a hallway calibration environment in which a vehicle traverses a drive path along which the vehicle is flanked by vehicle sensor calibration targets.

The hallway calibration environment 300, which may also be referred to as a tunnel calibration environment, includes a thoroughfare 305 through which a vehicle 102 drives, the thoroughfare 305 flanked on either side by targets detectable by the sensors 180 of the vehicle 102. The thoroughfare 305 may also be referred to as the drive path, the drive channel, the hallway, or the tunnel. Some of the targets are arranged in a left target channel 310 that is to the left of the vehicle 102 as the vehicle 102 traverses the thoroughfare 305. Others of the targets are arranged in a right target channel 315 that is to the right of the vehicle 102 as the vehicle 102 traverses the thoroughfare 305. In FIG. 3, the targets in the left target channel 310 and right target channel 315 are all checkerboard camera targets 200A as illustrated in FIG. 2A, but they may include any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors. The left target channel 310 and right target channel 315 may include a combination of different target types, similarly to the calibration environment of FIG. 6; the targets need not all be of a uniform type as illustrated in FIG. 3.

The vehicle 102 drives along the thoroughfare 305, stopping after incremental amounts, for example, every foot, every N feet, every meter, or every N meters, where N is a number greater than zero, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10. At each stop, the vehicle 102 captures data using each of its vehicle sensors, or at least each of the vehicle sensors that it intends to calibrate. The vehicle 102 stopping helps prevent issues caused by sensors running while the vehicle 102 is in motion, such as motion blur or rolling shutter issues in cameras. The vehicle 102 stopping also ensures that sensors can capture data while the vehicle 102 is in the same position, which may be important for extrinsic calibration of two or more sensors with respect to each other so that a location within data gathered by a first vehicle sensor (e.g., a range sensor such as a LIDAR or radar sensor) can be understood to correspond to a location within data gathered by a second vehicle sensor (e.g., a camera). The vehicle 102 may in some cases traverse the thoroughfare 305 multiple times, for example N times in each direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

The sensor targets illustrated in FIG. 3 are illustrated as each mounted on separate easel-style stands. Other types of stands are also possible, such as the type illustrated in FIG. 4 and FIG. 5. Furthermore, multiple sensor targets, of the same type or of different types may be supported by each stand (as in FIG. 4 and FIG. 5) even though this is not illustrated in FIG. 3.

The sensor targets illustrated in FIG. 3 are illustrated such that some are positioned closer to the thoroughfare 305 while some are positioned farther from the thoroughfare 305. Additionally, while some targets in FIG. 3 are facing a direction perpendicular to the thoroughfare 305, others are angled up or down with respect to the direction perpendicular to the thoroughfare 305. While the sensor targets illustrated in FIG. 3 all appear to be at the same height and all appear to not be rotated about an axis extending out from the surface of the target, it should be understood that the sensor targets may be positioned at different heights and may be rotated about an axis extending out from the surface of the target as in the targets of FIG. 4 and FIG. 5. Together, the distance from the thoroughfare 305, the direction faced relative to the thoroughfare 305, the clustering of targets, the height, and the rotation about an axis extending out from the surface of the target may all be varied and modified to provide better intrinsic and extrinsic calibration. That is, these variations assist in intrinsic calibration in that collection of data with representations of targets in various positions, rotations, and so forth ensures that targets are recognized as they should be by any sensor, even in unusual positions and rotations, and that any necessary corrections be performed to data captured by sensors after calibration. These variations assist in extrinsic calibration in that the different positions and rotations and so forth provide more interesting targets for range sensors, such as lidar, radar, sonar, or sodar, and allow range sensors to aid in interpretation of optical data collected by a camera of the vehicle 102.

While the thoroughfare 305 of the hallway calibration environment 300 of FIG. 3 is a straight path, in some cases it may be a curved path, and by extension the left target channel 310 and right target channel 315 may be curved to follow the path of the thoroughfare 305.

While the hallway calibration environment 300 is effective in providing an environment with which to calibrate the sensors 180 of the vehicle 102, it is inefficient in some ways. The hallway calibration environment 300 is not space efficient, as it occupies a lot of space. Such a hallway calibration environment 300 is best set up indoors so that lighting can be better controlled, so the hallway calibration environment 300 requires a large indoor space, and by extension, a lot of light sources, which is not energy-efficient or cost-efficient. Because of how much space the hallway calibration environment 300 takes up, it is more likely to have to be taken down and set back up again, affecting consistency of calibration between different vehicles whose sensors are calibrated at different times. Further, because the setup of the hallway calibration environment 300 requires the vehicle 102 to drive through it, different vehicles 102 might be aligned slightly differently in the thoroughfare 102, and might drive a slightly different path through the thoroughfare 102, and might stop at slightly different spots and/or frequencies along the drive, due to manufacturing differences in the vehicle 102 and due to human error in setting the vehicle 102 up, all of which affects consistency of the calibration. Trying to correct for all of these potential inconsistencies, and turning the vehicle around to move it through the hallway calibration environment 300 multiple times, is time and labor intensive, making the hallway calibration environment 300 time-inefficient. Additionally, because the targets are primarily to the left and right sides of the vehicle 102 hallway calibration environment 300, vehicle sensors might not be as well calibrated in the regions to the front and rear of the vehicle. Using a thoroughfare 305 with some turns can help alleviate this, but again causes the hallway calibration environment 300 to take up more space, increasing space-inefficiency.

FIG. 4 illustrates a perspective view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by vehicle camera calibration targets rotates a vehicle so that the vehicle can perform intrinsic calibration of its camera sensors.

The dynamic scene calibration environment 400 of FIG. 4 includes a motorized turntable 405 with a platform 420 that rotates about a base 425. In some cases, the platform 420 may be raised above the floor/ground around the turntable 405, with the base 425 gradually inclined up to enable the vehicle 120 to drive up the base 425 and onto the platform 420, or to drive off of the platform 420 via the base 425. A vehicle 102 drives onto the platform 420 of the turntable 405, and the motors actuate to rotate platform 420 of the turntable 405 about the base 425, and to thereby rotate the vehicle 102 relative to the base 425 (and therefore relative to the floor upon which the base 425 rests). While the arrows on the turntable 405 show a counter-clockwise rotation of the platform 420, it should be understood that the platform 420 of the motorized turntable 405 can be actuated to rotate clockwise as well. The turntable 405 is at least partially surrounded by targets mounted on stands 410. In FIG. 4, the illustrated targets are all checkerboard-patterned camera calibration targets 200A as depicted in FIG. 2A, allowing for calibration of cameras of the vehicle 102. In other embodiments, such as in FIG. 5, the targets around the motorized turntable may include any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors.

In one embodiment, the platform 420 of the motorized turntable 405 may be rotated by predetermined intervals (measured in degrees/radians or an amount at a time), for example intervals of ten degrees, in between point the turntable stops so that the vehicle 102 can capture data with its sensors 180. The platform 420 of the motorized turntable 405 can start and stop in this manner, and can eventually perform a full 360 degree rotation in this manner. The motorized turntable 405 may in some cases perform multiple full 360 degree rotations in one or both rotation directions (clockwise and counterclockwise), for example N rotations in each rotation direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

FIG. 5 illustrates a perspective view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by combined vehicle range sensor and vehicle camera calibration targets rotates a vehicle so that the vehicle can perform extrinsic calibration of its range sensors and camera sensors.

The dynamic scene calibration environment 500 of FIG. 5 includes the same motorized turntable 405 as in FIG. 4. A vehicle 102 drives onto the platform 420 of the turntable 405, and the motors actuate to rotate the platform 420 of the turntable 405, and thereby rotate the platform 420 (and the vehicle 102) about the base 425, clockwise, counter-clockwise, or one then the other. The turntable 405 is at least partially surrounded by targets mounted on stands 410. In FIG. 5, the illustrated targets include both a set of combined range/camera extrinsic calibration targets 250 as depicted in FIG. 2E and a set of trihedral radar calibration targets 220 as depicted in FIG. 2D. Each stand 410 mounts two combined range/camera extrinsic calibration targets 250 and one trihedral radar calibration targets 220, which in some cases may be a known distance from the combined range/camera extrinsic calibration targets 250 on the stand 410, permitting extrinsic calibration between the radar and the range sensor (lidar, radar, sonar, sodar) and/or camera calibrated using the combined range/camera extrinsic calibration targets 250.

As the vehicle 102 rotates about the base 425 on the platform 420 of the motorized turntable 405, and/or during stops between rotations, the vehicle 102 and its computer 110 can detect the combined range/camera extrinsic calibration targets 250 using both its range sensors (e.g., lidar, etc.) and cameras by detecting the apertures 225 with the range sensors and the markings 230 around the apertures and the target identifier markings 235 with the cameras. In doing so, the vehicle 102 and its computer 110 can detect a center of the circular aperture 225 easily, since range sensors such as lidar typically provide a point cloud of depth measurements that can help identify where the widest parts of each circle are. The rings 230 detected by the camera will also have the same centers as the apertures, so the range sensor and camera know they are looking at the exact same locations for each of these center points. Thus, the camera and range sensor may be extrinsically calibrated so that their positional awareness of the surroundings of the vehicle 102 can be positionally aligned. The extrinsic calibration may, in some cases, output one or more matrices (e.g., one or more transformation matrices) used for transforming a camera location to a range sensor location or vice versa, via translation, rotation, or other transformations in 3D space. Calibration affects interpretation of data captured by the sensors after calibration is complete. The transformation(s) that are generated during this extrinsic calibration can include one or more types of transformations, including translations, stretching, squeezing, rotations, shearing, reflections, perspective distortion, distortion, orthogonal projection, perspective projection, curvature mapping, surface mapping, inversions, linear transformations, affine transformations, The translational and rotational transformations may include modifications to position, angle, roll, pitch, yaw, or combinations thereof. In some cases, specific distortions may be performed or undone, for example by removing distortion (e.g., parabolic distortion) caused by use of a specific type of lens in a camera or other sensor, such as a wide-angle lens or a fisheye lens or a macro lens.

The transformation(s) generated by the computer 110 of the vehicle 102 may be used for extrinsic calibration of a first sensor (e.g., the camera) with respect to a second sensor (e.g., LIDAR or RADAR or SONAR or SODAR or another range sensor), so that the computer 102 can map positions identified in the data output from each sensor to the real world environment around the vehicle 102 (e.g., in the field of view of the sensors 180 of the vehicle 102) and relative to each other, based on known relative positions of features identified within the outputs of each sensor. Such features may include the visual markings of the combined target 250 as identified by the camera, the apertures as identified by the range sensor, and optionally a trihedral target 220 affixed near or on the target 250 as in the environment 500 of FIG. 5. For example, if translation of positions in data captured by the first sensor to positions in the real world around the vehicle are already clear through intrinsic calibration, but translation of positions in data captured by the second sensor to positions in the real world around the vehicle are already clear through intrinsic calibration (or vice versa), then the transformation generated through this extrinsic calibration can translate positions in data captured by the second sensor to positions in the real world around the vehicle based on (1) the position in the real world around the vehicle of the data from the first sensor, and (2) the relative positioning of the position in the data from the first sensor and the position in the data from the second sensor. Thus, a sensor that has not been intrinsically calibrated can still be calibrated extrinsically relative to another sensor, and can still benefit from the increase in accuracy granted by the intrinsic calibration of that other sensor.

The trihedral targets 220 can also have a known distance from the combined range/camera extrinsic calibration targets 250, and in some cases specifically from the centers of the apertures 225 and rings 230 of the targets 250, allowing extrinsic calibration between the range sensor (e.g., radar) that recognizes the trihedral targets 220 and the range sensor (e.g., lidar) that recognizes the apertures 225 and the camera that recognizes the rings/markings 230.

In other embodiments, the targets around the motorized turntable 405 may include any other type of target discussed herein that is used to calibrate any vehicle sensor or combination of vehicle sensors, such as the target 200A of FIG. 2A, the target 200B of FIG. 2B, the target 200C of FIG. 2C, the target 220 of FIG. 2D, the target 250 of FIG. 2E, targets with heating elements detectable by infrared sensors of the vehicle 102, targets with speakers detectable by microphones of the vehicle 102, targets with reflective acoustic properties detectable by SONAR/SODAR/ultrasonic/infrasonic sensors of the vehicle 102, or some combination thereof.

The stands 410 used in FIG. 3-6 may include any material discussed with respect to the substrate 205, such as paper, cardboard, plastic, metal, foam, or some combination thereof. In some cases, certain stands may be made of a plastic such polyvinyl chloride (PVC) to avoid detection by certain types of range sensors, such as radar, which detect metal better than plastic.

In one embodiment, the platform 420 of the motorized turntable 405 may be rotated about the base 425 by predetermined intervals (measured in degrees/radians or an amount at a time), for example intervals of ten degrees, in between point the turntable stops so that the vehicle 102 can capture data with its sensors 180. The intervals may be N degrees, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20. The platform 420 of the motorized turntable 405 can start and stop its rotation via activation and deactivation of its motor(s) 730 in this manner, and can eventually perform a full 360 degree rotation in this manner. The platform 420 of the motorized turntable 405 may in some cases perform multiple full 360 degree rotations about the base 425 in one or both rotation directions (clockwise and counterclockwise), for example N rotations in each rotation direction, where N is, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20.

FIG. 6 illustrates a top-down view of a dynamic scene calibration environment in which a turntable that is at least partially surrounded by both vehicle camera calibration targets and combined vehicle range sensor and vehicle camera calibration targets rotates a vehicle so that the vehicle can perform both intrinsic calibration of its camera sensors and extrinsic calibration of its range sensors and camera sensors.

The dynamic scene calibration environment 600 of FIG. 6 is, in some ways, of a combination of the dynamic scene calibration environment 400 of FIG. 4 and the dynamic scene calibration environment 500 of FIG. 5 in that three types of targets are positioned around the motorized turntable 405. These three types of targets include the checkerboard-patterned camera calibration targets 200A used in the dynamic scene calibration environment 400 of FIG. 4 and the trihedral radar calibration targets 220 and combined extrinsic calibration targets 250 used in the dynamic scene calibration environment 500 of FIG. 5. In FIG. 6, all three types of targets stand separately from each other.

Any of the stands used in FIG. 3-6 may be any type of stands, such as easel-type stands, tripod-type stands, or the rod stands 410 with wide bases seen in FIG. 4 and FIG. 5. Any of the stands used in FIG. 3-6 may include paper, cardboard, plastic, metal, foam, or some combination thereof as previously discussed. The stands in some cases may include motors and actuators enabling positions and/or angles of rotation of targets to be manipulated, for example to be more clearly visible given lighting conditions supplied by light sources 620, or to access a region of a point of view of a particular vehicle sensor from which that vehicle sensor has not captured enough data and therefore around which the vehicle sensor is under-calibrated. In some cases, these motors and their actuators may be controlled wirelessly by the vehicle 102 and/or scene surveying system 610 as discussed further with respect to FIG. 7 and FIG. 14.

The dynamic scene calibration environment 600 of FIG. 6 also includes a scene surveying system 610, which may include one or more cameras, one or more range sensors (e.g., radar, lidar, sonar, sodar, laser rangefinder). This may take the form of a robotic total station (RTS). While the vehicle 102, its sensors 180, and the targets are enough to perform intrinsic calibration of sensors to correct for distortions, for example, and to perform extrinsic calibration of sensors to align locations within data captured by different sensors, for example, in some cases more information may be needed to understand how far these locations within data captured by different sensors are from the vehicle itself. The scene surveying system 610 captures visual and/or range data of at least a subset of the dynamic scene calibration environment 600, including the motorized turntable 410, at least some of the targets, and the vehicle 102 itself. Key points on the vehicle 120 may be tracked to identify the current pose (rotation orientation/position) of the vehicle 102 (and therefore of the platform 420 about the base 425). Data captured by the scene surveying system 610 can also be sent to the vehicle 102 and used to verify the data captured by the sensors and the intrinsic and extrinsic calibrations performed based on this data. The dynamic scene calibration environment 600 also includes several light sources 620 set up around the turntable 405. These light sources 620 may include any type of light source, such as incandescent bulbs, halogen bulbs, light emitting diodes (LEDs), and so forth. In some cases, the light sources 620 may include stage lights or spotlights. Mirrors and/or lenses may be used to manipulate light from the light sources 620 to relatively evenly illuminate the targets and/or the dynamic scene calibration environment 600 as a whole. Additional light sources may be positioned over the turntable 405 or behind some or all targets, especially if targets use a transparent or translucent substrate. The light sources 620 are used to improve readings from cameras of the vehicle and of the scene surveying system 610, and can in some cases be controlled wirelessly by the vehicle 102 and/or scene surveying system 610 as discussed further with respect to FIG. 7 and FIG. 14.

By including targets for intrinsic sensor calibration as well as targets for extrinsic calibration around the turntable 405, the sensors 180 of the vehicle 102 can receive a comprehensive calibration in the dynamic scene calibration environment 600. The dynamic scene calibration environments 400, 500, and 600 of FIGS. 4-6 have several advantages over the static hallway calibration environment 300 of FIG. 3. First, the dynamic scene calibration environment is more space-efficient, as it only requires space for the vehicle turntable 405, space for some targets, light sources 620, and scene surveying system 610. There is no need to clear space for a thoroughfare 305 or other long path. Because fewer light sources 620 are needed to light a smaller space like the dynamic scene calibration environment 600 than a larger space like the hallway calibration environment 300, the dynamic scene calibration environment 600 is more energy-efficient in terms of lighting. Because the vehicle engine can be turned off after the vehicle is on the turntable 405, the dynamic scene calibration environment 600 is more energy-efficient in terms of vehicular power usage. Because changing rotation directions is considerably quicker than performing a U-turn in a vehicle to change driving directions, and because a smaller space is more likely to remain untouched and therefore won't need take-down and setup after and before each use, the dynamic scene calibration environment 600 is more time-efficient. Because the target and lighting setup, and the movement of the vehicle 102, can be better controlled, the calibration results are more consistent across vehicles 102, which further allows calibration configurations of different vehicles 102 to be more readily compared and outliers identified as potentially symptomatic of a sensor defect or a miscalibration issue. Use of guide railings 810 as illustrated in and discussed with respect to FIG. 8A-8D further increases consistency of calibration by increasing consistency of positioning of the vehicle 102 along the turntable 405. The dynamic scene calibration environment 600 also allows the sensors 180 of the vehicle 102 to capture data all around the vehicle 102—including in the front and rear of the vehicle 102, which receive less data in the hallway calibration environment 300.

FIG. 7 illustrates a system architecture of an dynamic scene calibration environment.

The system architecture 700 of the dynamic scene calibration environment of FIG. 7 includes a number of main elements, each with sub-components. The main elements include the autonomous vehicle 102, a dynamic scene control bridge 710, the motorized turntable system 450, a lighting system 760, a target control system 770, a scene surveying system 610, and a power supply system 790.

The autonomous vehicle 102 includes the one or more sensors 180, the one or more internal computing devices 110, one or more wireless transceivers 705 (integrated with communication service 116), and any other elements illustrated in and discussed with respect to FIG. 1. The sensors 180 may in some cases include one or more GPRS receivers, Bluetooth® beacon-based positioning receivers, an inertial measurement unit (IMU), one or more cameras, one or more lidar sensors, one or more radar sensors, one or more sonar sensors, one or more sodar sensors, and/or one or more other sensors discussed herein, which may in some cases be used to identify when the vehicle 102 is not on the platform 420, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., as in FIG. 8B), when the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The computing device 110 of the vehicle 102 (or its sensors 180) may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces, for instance through the wireless transceiver 705, to the bridge 710, the computing device 110 of the vehicle 102 (if the communication is straight from the sensors 180), and/or the computing device 745 of the motorized turntable system 405 to convey any of these observations/detections/identifications by the sensors 180, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof.

The dynamic scene control bridge 710 includes one or more computing devices 715 and one or more wired and/or wireless transceivers 720. The dynamic scene control bridge 710 is optional, but can serve as a “middleman” or “router” between the autonomous vehicle 102 and the remaining main elements of the system architecture 700, such as the dynamic scene control bridge 710, the motorized turntable system 450, the lighting system 760, the target control system 770, the scene surveying system 610, and the power supply system 790. The dynamic scene control bridge 710 can in some cases convert file formats, perform mathematical operations such as operation conversions, or otherwise interpret instructions or data as necessary so that the vehicle 120 can successfully communicate with other elements. In fact, the dynamic scene control bridge 710 can perform similar conversions, mathematical operations, or interpretations in its role as a middleman between any two or more devices of the architecture 700.

The motorized turntable system 405 includes a turntable structure 725 as well as one or more motors, encoders, actuators, and/or gearboxes 730 for actuating rotation of the turntable structure 725 while the vehicle 102 is on it. The motorized turntable 725 may include a platform 420, which is a surface upon which the vehicle 102 rests during calibration. The platform 420 is rotatable about a base 425 of the motorized turntable structure 725, with one or more motors 730 that, when actuated or activated, rotate the platform 420 about the base 425, and which stop the rotation of the platform 420 about the base 425 when the motors 730 are deactivated. For example, the one or more motors 730 may rotate the platform 420 about the base 425 from a first rotational orientation to a second rotational orientation, or alternately back to the first rotational orientation (e.g., if the rotation is a 360 degree rotation or a multiple thereof). A rotational orientation of the platform 420 relative to the base 425, or of the vehicle 102 relative to the base 425, may alternately be referred to as a rotational position. The motorized turntable system 405 may include one or more sensors 735, such as pressure sensors, for example to identify whether or not the vehicle 102 is on the turntable structure 725, whether or not the vehicle 102 is positioned correctly on the turntable structure 725, or how the vehicle's weight is distributed generally or across the platform 420's top surface (which is in contact with the wheels and/or tires vehicle 102) of the turntable structure 725. The sensors 735 may in some cases identify when the platform 420 has no vehicle 102 on it, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., as in FIG. 8B), when the platform 420 and/or the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The motorized turntable system 405 may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces to the bridge 710, vehicle 102, and/or the computing device 745 of the motorized turntable system 405 to convey any of these observations/detections/identifications by the sensors 735, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof. The controller 740 may be used to control the actuation of the motors, encoders, actuators, and/or gearbox(es) 730, for example to control a speed or rate or angular velocity of rotation, an angular acceleration (or deceleration) of rotation, a direction of rotation (e.g., clockwise or counterclockwise), or some combination thereof. The motorized turntable system 405 includes one or more computing devices 745 and one or more wired and/or wireless transceivers 750, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The lighting system 760 includes one or more light sources 620 and one or more motors and/or actuators 762 for activating or turning on each of the light sources 620, disabling or turning off each of the light sources 620, fading or dimming each of the light sources 620, brightening each of the light sources, or moving each of the light sources 620 with an actuated motor (e.g., to shine on a particular target). The lighting system 760 includes one or more computing devices 764 and one or more wired and/or wireless transceivers 766, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The target control system 770 includes one or more targets and target support structure 772. The targets may include one or more of any of the targets 200A, 200B, 200C, 220, and/or 250 illustrated in FIG. 2A-2E, any other target described herein, any other sensor calibration target, or some combination thereof. The target support structures may include easel-type support structures, tripod-type support structures, or the rod-type support structures 410 with wide bases seen in FIG. 4 and FIG. 5. The target support structures may include any material discussed with respect to the substrate 205, such as paper, cardboard, plastic, metal, foam, or some combination thereof. In some cases, certain stands may be made of a plastic such polyvinyl chloride (PVC) to avoid detection by certain types of range sensors, such as radar, which detect metal better than plastic.

The targets and/or support structures 720 may in some cases be motorized, and as such, the target control system 770 may include motors and actuators 774 that it can use to move the targets, for example as requested by the vehicle 102 to optimize calibration. For example, the target support structures may include a robotic arm with ball joints and/or hinge joints that may be actuated using the motors and actuators 774 to translate a target in 3D space and/or to rotate a target about any axis. The motors and actuators 773 may alternately only control a single type of movement for a particular target, for example by enabling a target to rotate about the rod of a stand 410. The target support structure 772 may also include wheels or legs, which may be actuated by the motors 774 to enable the entire target support structure 772 to move, and with it, the target(s) it supports. The target control system 770 includes one or more computing devices 776 and one or more wired and/or wireless transceivers 778, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The scene surveying system 610 includes a surveying device support structure 780, such as a tripod or any other structure discussed with respect to the target support structure 772, and one or more sensors 782 coupled to the support structure 780. The sensors 782 of the scene surveying system 610, like the sensors 180 of the vehicle 102, may include one or more cameras of any type (e.g., wide-angle lens, fisheye lens), one or more range sensors (e.g., radar, lidar, emdar, laser rangefinder, sonar, sodar), one or more infrared sensors, one or more microphones, or some combination thereof. Using these, the scene surveying system 610 can capture a representation of the entire dynamic scene, including the vehicle 102, allowing determination of distances between the vehicle 102 and various targets. In some cases, either the vehicle 102 or the scene surveying system 610 or both may request adjustment of lighting through the lighting system 760 and/or adjustment of target positioning via the target control system 770. The scene surveying system 610 includes one or more computing devices 784 and one or more wired and/or wireless transceivers 784, through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700. In some cases, feature tracking and/or image recognition techniques applied using the computing device 784 may be used with the a camera and/or the radar, lidar, sonar, sodar, laser rangefinder, and/or other sensors 782 of the scene surveying system 610 to identify when the platform 420 has no vehicle 102 on it, when the vehicle 102 is on the platform 420, when the vehicle 102 in a defined position on the platform (e.g., as in FIG. 8B), when the platform 420 and/or the vehicle 102 have begun rotating from a stopped position, and/or when the platform 420 and/or the vehicle 102 have stopped rotating. The scene surveying system 610 may then automatically communicate one or more signals or messages through wired and/or wireless communication interfaces to the bridge 710, vehicle 102, and/or motorized turntable system 405 to convey any of these observations/detections/identifications by the scene surveying system 610, which may be used to trigger various actions, such as rotation or stop of rotation of the turntable 405, collection of sensor data or stop of collection of sensor data at the vehicle 102, or some combination thereof. In some cases, the scene surveying system 610 may simply be referred to as a camera or as another sensor that the scene surveying system 610 includes.

The power supply system 790 may include batteries, generators, or may plug into an outlet and into the power grid. The power supply system 790 may supply power to the various elements and components of the system architecture 700, including at least the dynamic scene control bridge 710, the motorized turntable system 450, the lighting system 760, the target control system 770, and the scene surveying system 610. The power supply system 790 may also charge the vehicle 102 before, during, and/or after calibration, for example if the vehicle 102 is electric or hybrid. The power supply system 790 may also intelligently scale voltage, amperage, and current as appropriate for each element and component of the system architecture 700, and to do so it may include a computing system 1500 (not pictured). It may also include a wired and/or wireless transceiver (not pictured) through which it may interact with the vehicle 102, the dynamic scene control bridge 710, or any other element in the architecture 700.

The computing devices 110, 715, 745, 764, 776, and 784 may each, at least in some cases, include at least one computing system 1500 as illustrated in or discussed with respect to FIG. 15, or may include at least a subset of the components illustrated in FIG. 15 or discussed with respect to computing system 1500.

FIG. 8A illustrates a top-down view of a turntable with a vehicle guide rail, and a vehicle driving onto the turntable centered relative to the vehicle guide rail.

The motorized turntable system 405 of FIG. 8A includes a guide railing 810 on the platform 420 of the turntable 405, particularly on a top surface of the platform 420, so that the guide railing 810 rotates along with the rest of the platform 420 about the base 425 when the turntable motor 730 is activated. The guide railing 810 includes two substantially parallel straight rails 840 with a space in between, illustrated in FIG. 8A as parallel vertically-oriented lines, the space between the two straight rails 840 corresponding to a distance between two wheels and/or tires of the vehicle 102 (e.g., left and right front wheels/tires of the vehicle 102 and/or left and right rear wheels/tires) in such a way that the vehicle 102 can straddle the two straight rails 840 with at least one left wheel and/or tire on a left side of the left straight rail 840 and at least one right wheel and/or tire on a right side of the right straight rail 840. The rails of the guide railing 810—that is, the two straight rails 840 and the two slanted rails 845—may extend from the top surface of the platform 420 in a direction perpendicular to the top surface of the platform 420.

At one end, these parallel rails end in a “stop” member 850, which may include a rail, a wall, a bump, or an inclined ramp, the stop member 850 gently stopping the vehicle 102 from continuing further forward once the vehicle has reached a defined position astride the two straight rails 840. That is, each stop member 850 may include a vertical wall or rail that extends in a direction perpendicular to the top surface of the platform 420, or an inclined ramp or bump that gradually increases in height (height being a direction perpendicular to the surface of the platform 420) along a direction in which the vehicle 102 is driving onto or along the platform 420 to form an incline that, if the vehicle 102 were to drive at least partially onto the incline, would push the vehicle 102 backward. The incline may be straight, have a convex curve, or have a concave curve. The incline may be part of a bump that goes up in height and then back down, such as a speed bump. That is, regardless of the form of the stop members 850, if the vehicle 102 reaches the defined position at which it is pictured in FIG. 8B and attempts to continue driving toward the one or more stop members 850, the vehicle 102 will come into contact with one or more stop members 850, which will push the vehicle back into the defined position, either gently using an incline or more forcefully using a wall or rail. The vehicle 102 may drive forwards onto the platform 420 and toward the stop members 850 as illustrated in FIG. 8A (i.e., with the front side of the vehicle 102 facing the direction of driving and the stop members 850), or may reverse onto the platform 420 and toward the stop members 850 (e.g., drive with the rear side of the vehicle 102 facing the direction of driving and the stop members 850). A wall or rail included in a stop member 850 may in some cases be padded using foam, silicone, rubber, or another soft or bouncy material that cushions the force applied by the stop member 850 on the vehicle 102 and the force applied by the vehicle 102 on the stop member 850. The stop members 850 of the guide railing 810 of FIG. 8A are illustrated as two relatively short horizontal lines (perpendicular to the parallel vertical guide rails) at the top endpoints (i.e., posterior ends) of the parallel straight rails 840 of the guide railing 810. In some cases, one of the two stop members 850 may be missing, as stopping one tire/wheel from progressing may be sufficient to stop the vehicle 102 from moving beyond the stop member 850. While the two stop members 850 are illustrated as a separate member for each of the two straight rails 840, in some cases (not pictured), a single longer stop member 850 can be used that bridges the gap between the straight rails 840 and comes into contact with the posterior ends of both straight rails 840 in a direction that is perpendicular to both straight rails, extending beyond the posterior ends of one or both straight rails 840.

The other end (i.e., the anterior end) of each of the two parallel straight rails 840 ends in a slanted rail 845. Relative to the two parallel straight rails 840, the two slanted rails 845 slant towards one another. The slanted rails 845 are generally not parallel to one another or to the straight rails 840, though may include portions that are parallel to one or both (e.g., when the slanted rails 845 are curved). That is, the leftmost slanted rail 845 slants to the right as it proceeds in further downward (i.e., in a more anterior direction), while the rightmost slanted rail 845 slants to the left as it proceeds in further downward (i.e., in a more anterior direction). In the guidance rail 810 illustrated in FIG. 8A, the two slanted rails 845 are straight as well, and approach a single point (i.e., a vertex) at which they meet/converge. That point at which the slanted rails 845 meet/converge may be along a center line 805/815 that is parallel to the two straight rails 840 and that is centered (i.e., equidistant) between the two straight rails 840. Accordingly, the two slanted rails 845, together, form a “V” shape. In some cases (not illustrated), the two slanted rails 845 need not meet at the point they both approach, thereby remaining separate and effectively removing a bottom portion/fraction of the “V” shape that includes the vertex. In some cases (not illustrated), the vertex of the “V,” the vertices between the slanted rails 845 and the straight rails 840, and/or any other vertices formed by the slanted rails 845 and/or the straight rails 840 may be smoothed out so as not to present a risk of puncturing or otherwise damaging a tire or wheel of the vehicle 102. In some cases (not illustrated), the two slanted rails 845 are curved rather than straight, for example forming a “U” shape rather than a “V” shape, or at least a top portion of a “U” shape. That is, each of the slanted rails 845 may include one or more curves that each form a portion of a circle or a portion of an ellipse, each curve of which may for example be concave facing inward (i.e., facing the space between the two parallel straight rails 840) and convex facing outward (i.e., facing the base 425 of the turntable and the rest of the calibration environment) or vice versa. In some cases, each of the slanted rails 840 may include one or more straight portions and one or more curved portions, and may for example form a curve similar to a curve formed by graphing a tangent function (e.g., f*tan(x) where f is a positive or negative constant) in a 2-dimensional X-Y space.

For structural integrity, the guide railing 810 may also include optional braces (not pictured) connecting the left and right straight rails 840 to one another through the space in between the two straight rails 840. Similar braces may exist between the two slanted rails 845, connecting the two slanted rails 845. Similar braces may exist between the two stop members 850, connecting the two stop members 850. The braces may include braces that are perpendicular to the parallel vertical guide rails or diagonal relative to the parallel vertical guide rails (e.g., forming one or more cross (“X”) structures). The braces may include braces that are perpendicular to at least a portion of one or both of the slanted rails 845 or diagonal relative to at least a portion of one or both of the slanted rails 845.

The vehicle 102 of FIG. 8A approaches the motorized turntable system 405 along a centered path 805, represented by dashed line arrow, that is centered with respect to the turntable 405 and the guide railing 810. As a result, the vehicle 102 will successfully drive onto the center of the platform 420 of the motorized turntable 405, eventually reaching a defined position in which the wheels and/or tires of the vehicle 102 straddle (i.e., are positioned astride) the two straight rails 840 and abut the stop members 850, the vehicle 102 illustrated in this defined position in FIG. 8B. The defined position represents the position that the guide railing 810 guides the vehicle 102 into.

In some cases (not pictured), the guide railing 810 may intentionally be positioned off-center (horizontally and/or vertically) relative to the center of the platform 420 of the motorized turntable system 405. For example, the guide railing 810 may be moved further forward relative to the platform 420 to accommodate and/or center a larger or longer vehicle 102 than the illustrated sedan-style vehicle 102 (e.g., a van, truck, or limousine) on the platform 420, or further backward relative to the platform 420 to accommodate and/or center a smaller or shorter vehicle 102 than the illustrated sedan-style vehicle 102 (e.g., a compact automobile, buggy, or all-terrain vehicle) on the platform 420. The guide railing 810 may be moved left or right as well, as it may be desirable for the vehicle 102 to move translationally in space during certain types of sensor calibrations rather than to just rotate about an axis that passes through the vehicle, effectively rotating in small circular “laps” within a radius of the platform 420 but with the benefit of the precision motor control of the turntable 405 (as opposed to using the vehicle 102's engine for propulsion) and of not having to consume fuel or battery power of the vehicle 102. In some cases, the guide railing 810 may be adjusted dynamically. To achieve such adjustability in a dynamic fashion, the guide railing 810 itself may be attached to the platform 420 via additional rail tracks (not shown) along the platform 420 that are optionally recessed into the platform, the rail tracks along the platform 420 allowing the guide railing 810 to be slid along the rail tracks relative to the surface of the platform 420, with one or more latches and/or magnets and/or screws used to lock the guide railing 810 in a particular position along the rail tracks along the platform 420. Movement of the guide railing 810 along such rail tracks along the platform 420 may also be activated and/or deactivated using one or more motors that may be controlled by the motorized turntable system 405 and/or by the vehicle 102 and/or otherwise as discussed with respect to actuation of the motors, encoders, actuators, and/or gearbox(es) 730.

FIG. 8B illustrates a vehicle having successfully driven onto the turntable centered along the vehicle guide rail, the vehicle thus centered with respect to the turntable.

The vehicle 102 in FIG. 8B, which is illustrated as transparent with a dashed line outline and grey-filled wheels/tires, has driven onto the motorized turntable system 405. Through the transparent vehicle outline, we can see that the wheels and tires of the vehicle 102 are adjacent to the outsides of the parallel rails of the guide railing 810, and the stop member 850 has stopped the vehicle 102 at the defined position, which in FIG. 8B is in the center of the platform of the turntable 405, but may alternately be positioned further forward, backward, and/or to one of the sides of the platform 420 as discussed with respect to FIG. 8A.

While the two straight rails 840 illustrated in FIGS. 8A-8D are only long enough for two wheels and/or tires of the vehicle 102 (either the front left and right wheels/tires or the rear left and right wheels/tires) to stand astride the two straight rails 840 while the vehicle 102 is in the defined position, in some cases the two straight rails 840 may be long enough so that all four or more wheels and/or tires of a vehicle 102 stand astride the two straight rails 840 while the vehicle 102 is in the defined position. That is, if the vehicle is a six-wheeler or eight-wheeler or ten-wheeler truck or other long vehicle 102, all of the left and right pairs of wheels, or any subset of the pairs of wheels, can stand astride the two straight rails 840 while the vehicle 102 is in the defined position. FIG. 8C illustrates a top-down view of a turntable with a vehicle guide rail, and a vehicle driving onto the turntable off-center relative to the vehicle guide rail.

The vehicle 102 of FIG. 8C approaches the platform of the motorized turntable system 405 along an un-centered path 820, represented by dashed line arrow, which is off-center with respect to the turntable 405 and the guide railing 810, as is visible when compared to the center line 815, represented by a dotted line. The center line 815 is a line representing the centered path 805 from FIG. 8A and thus represents center with respect to the platform of the turntable 405 and the guide railing 810 and the defined position of the vehicle 102 in FIG. 8B. The center line 815 is illustrated in FIG. 8C to visibly highlight that the un-centered path 820 is off-center with respect to the platform of the turntable 405 and the guide railing 810 and the defined position of the vehicle 102 in FIG. 8B. As a result, the vehicle 102 will drive onto the platform of the motorized turntable 405 off-center with respect to the platform of the turntable 405 and the guide railing 810 and the defined position of the vehicle 102 in FIG. 8B, and will approach the guide railing 810 off-center off-center with respect to the platform of the turntable 405 and the guide railing 810 and the defined position of the vehicle 102 in FIG. 8B as visible in FIG. 8D.

FIG. 8D illustrates a vehicle having driven partially onto the turntable while off-center relative to the vehicle guide rail, the vehicle guide rail guiding the vehicle's path to center the vehicle with respect to the turntable.

The vehicle 102 in FIG. 8D, which is illustrated as transparent with a dashed line outline and grey wheels/tires, has driven onto the motorized turntable system 405 off-center. Through the transparent vehicle outline, we can see that one of the wheels/tires of the vehicle 102 is contacting the slanted rails 845 (e.g., the “V” or “U” portion) of the guide railing 810 at a guidance point 830. If the vehicle 102 keeps driving forward relative to the position it is illustrated in FIG. 8D, the slanted rail 840 will push back against the vehicle 102 at the guidance point 830 (i.e., the contact point), as will further contact points along the same slanted rail 840 of the guide railing 810 as the vehicle 102 continues to progress forward (diagonally) pressing against the same slanted rail 840, and thus the slanted rail 840 of the guide railing 810 will automatically guide the vehicle 102 to the right toward the centered/defined position until the vehicle 102 reaches the substantially parallel vertical rail portions of the guide railing 810, after which the vehicle 102 can drive straight without encumbrance. Thus, through application of forward torque/force by the vehicle 102 on the slanted rail 845 at the guidance point 830, and through reciprocal force pushing back diagonally on the vehicle 102 from the guidance point 830 of the slanted rail 420 of the guide railing 810, eventually, the slanted rail 420 of the guide railing 810 will assist the vehicle 102 into a centered position with respect to the guide railing 810 and the platform 420 of the turntable 405, and the vehicle 102 will ultimately reach the defined position that the vehicle 102 is illustrated having reached in FIG. 8B after moving forward along the straight rails 840. While FIGS. 8C and 8D illustrate the effect of the guide railing 810 on centering the vehicle 810 along the platform 420 of the turntable system 405 when the vehicle 102 is approaching off-center to the left relative to the guide railing 810 by pushing the vehicle 102 to the right, a similar effect occurs when the vehicle 102 is approaching off-center to the right relative to the guide railing 810 (not illustrated) by pushing the vehicle 102 to the left. Thus, the guide railing 810 allows for easy and consistent positioning of the vehicle 102 at a defined position along the platform 420 of the turntable 405.

FIG. 9 is a flow diagram illustrating operation of a calibration environment.

At step 905, a high resolution map of calibration environment is generated. This may be performed using the scene surveying system 610, for example.

At step 910, all sensors 180 on the vehicle 102 are run in the calibration environment, for example at different rotation positions of the vehicle 102, which is rotated using motorized turntable 405. At step 915, the vehicle 102 generates a calibration scene based on its sensors 180, based on (a) synchronized sensor data, (b) initial calibration information, (c) vehicle pose information, and (d) target locations.

At step 915, the calibration systems in the vehicle read the calibration scene and: (a) detect targets in each sensor frame, (b) associate detected targets, (c) generate residuals, (d) solve calibration optimization problem, (e) validate calibration optimization solution, and (f) output calibration results. At step 925, the calibration results are tested against acceptable bounds and checked for numerical sensitivity. Successful calibration measurements are stored and logged, along with a minimal subset of data needed to reproduce them

FIG. 10 is a flow diagram illustrating operations for intrinsic calibration of a vehicle sensor using a dynamic scene.

At step 1005, a vehicle 102 is rotated into a plurality of vehicle positions over a course of a calibration time period using a motorized turntable 405. The vehicle 102 and motorized turntable 405 are located in a calibration environment. At step 1010, the vehicle 102 captures a plurality of sensor capture datasets via a sensor coupled to the vehicle over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the vehicle is at each of the plurality of vehicle positions.

At step 1015, an internal computing system 110 of the vehicle 102 receives the plurality of sensor capture datasets from the sensor coupled to the vehicle over a course of a calibration time period. At step 1020, the internal computing system 110 of the vehicle 102 identifies, in the plurality of sensor capture datasets, one or more representations of (at least portions of) the calibration environment that include representations of a plurality of sensor targets. The plurality of sensor targets are located at known (i.e., previously stored) positions in the calibration environment. At steps 1025-1030, the sensor is calibrated based on the representations of a plurality of sensor targets identified in the plurality of sensor capture datasets.

More specifically, at step 1025, the internal computing system 110 of the vehicle 102 identifies positions of the representations of the plurality of sensor targets within the one or more representations of (at least portions of) the calibration environment. If the sensor being calibrated is a camera, and the one or more representations of (portions of) the calibration environment are images, then the representations of the sensor targets may be areas within the one or more images comprised of multiple pixels, which the computing system 110 of the vehicle 102 can identify within the one or more images by generating high-contrast versions of the one or more images (i.e., “edge” images) that are optionally filtered to emphasize edges within the image, and by identifying features within the image comprised of one or more of those edges, the features recognizable as portions of the target. For example, the vertices and/or boxes in the checkerboard pattern 210A or the ArUco pattern 210B, curves or vertices in the crosshair pattern 210C, the circular ring marking patterns 230, or combinations thereof, may each be visually recognized as features in this way. Similarly, if the sensor being calibrated is a radar sensor, the radar sensor may recognize the trihedral shape 215 of the target 220 as a feature due to its reflective pattern that results in a high radar cross section (RCS) return. Similarly, if the sensor being calibrated is a lidar sensor, the lidar sensor may recognize the surface of the substrate 205 of the target 250 and the apertures 225/240 within the substrate 205 of the target 250, which may be recognized as a feature due to the sharp changes in range/depth at the aperture.

At step 1030, the internal computing system 110 of the vehicle 102 generates a transformation that maps (1) the positions of the representations of the plurality of sensor targets within one or more representations of (portions of) the calibration environment to (2) the known positions of the plurality of sensor targets within the calibration environment. Other information about the plurality of sensor targets, such as information storing visual patterns or aperture patterns of the sensor targets, may also be used to generate the transformation. For example, if the sensor being calibrated is a camera, and the computing device 110 knows that an image should have a checkerboard pattern 210A of a sensor target 200A, and recognizes a warped or distorted variant of the checkerboard pattern 210A (e.g., because the camera includes a fisheye lens or wide-angle lens), then the computing device 110 may use its knowledge of the way that the checkerboard should look, such as how far the vertices are from each other, that they should form squares, and that the squares are arranged in a grid pattern—to generate a transformation that undoes the distortion caused by the camera, thereby mapping the vertices detected in the image to real-world positions, at least relative to one another. In other words, the transformation includes one or more projective transformations of various 2-D image coordinates of sensor target features into 3-D coordinates in the real world and optionally back into 2-D image coordinates that have been corrected to remove distortion and/or other sensor issues.

Because the computing device 110 knows ahead of time exactly where the sensor targets are in the calibration environment, the transformation may also map the positions of the vertices in the image (and therefore the positions of the representations of the sensor targets in the representation of the calibration environment) to real-world positions in the calibration environment. The transformation(s) that are generated during intrinsic sensor calibration at step 1030 can include one or more types of transformations, including translations, stretching, squeezing, rotations, shearing, reflections, perspective distortion, distortion, orthogonal projection, perspective projection, curvature mapping, surface mapping, inversions, linear transformations, affine transformations, The translational and rotational transformations may include modifications to position, angle, roll, pitch, yaw, or combinations thereof. In some cases, specific distortions may be performed or undone, for example by removing distortion caused by use of a specific type of lens in a camera or other sensor, such as a wide-angle lens or a fisheye lens or a macro lens.

Step 1030 may be followed by step 1005 and/or by step 1010 if calibration is not yet complete, leading to gathering of more sensor capture datasets and further refinement of the transformation generated at step 1030. Step 1030 may alternately be followed by step 1045.

The previously stored information about the plurality of sensor targets may be from a high-definition map generated as in step 905 of FIG. 9, may be from a second sensor on the vehicle, or may simply be based on a prior understanding of the sensor targets. For example, the internal computing system 110 of the vehicle 102 understands what a checkerboard pattern 210A is and that the grid it forms ought to look include a grid of parallel and perpendicular lines under normal conditions. Because of this, the internal computing system 110 understands that if the representation it received from the sensor (camera) of a target with a checkerboard pattern 210A forms a grid warped or distorted by a wide-angle lens or fisheye lens, this difference (step 1030) can be corrected by the internal computing system 110 by correctively distorting or warping the image captured by the sensor (camera) by to reverse the warping or distortion in the representation until the corrected checkerboard looks like it should. This corrective warping or distortion is the correction generated in step 1035. The correction may also include a translation along X, Y, or Z dimensions, a rotation along any axis, a warp or distortion filter, a different type of filter, or some combination thereof

Steps 1045-1060 concern operations that occur after calibration is complete (i.e., post-calibration operations). At step 1045, the sensor of the vehicle captures a post-calibration sensor capture dataset after the calibration time period, after generating the transformation, and while the vehicle is in a second position that is not in the calibration environment. At step 1050, the computing device 110 of the vehicle 102 identifies a representation of an object within a representation of a scene identified within the post-calibration sensor capture dataset. At step 1055, the computing device 110 of the vehicle 102 identifies a position of the representation of the object within the representation of the scene. At step 1060, the computing device 110 of the vehicle 102 identifies a position of the object relative to the second position of the vehicle by applying the transformation to the position of the representation of the object within the representation of the scene.

Note that capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. While an initial correction is generated at step 1035, the vehicle 102 may continue to rotate, and its sensors 180 may continue to capture more sensor data, hence the dashed lines extending back up to steps 1005 and 1010 from step 1035. When step 1035 is reached a second, third, or N^(th) time (where N is any integer over 1), the correction generated the first time step 1035 was reached may be updated, revised, and/or re-generated based on the newly captured sensor data when step 1035 is reached again. Thus, the correction becomes more accurate as calibration continues.

For some additional context on intrinsic calibration: LIDAR intrinsic properties may include elevation, azimuth, and intensity. Camera intrinsic properties may be given as matrices based on camera region/bin, and may track projection, distortion, and rectification. All sensors' intrinsic properties (including LIDAR and camera) may include position in X, Y, and/or Z dimensions, as well as roll, pitch, and/or yaw.

FIG. 11 is a flow diagram illustrating operations for extrinsic calibration of two sensors in relation to each other using a dynamic scene.

At step 1105, a vehicle 102 is rotated into a plurality of vehicle positions over a course of a calibration time period using a motorized turntable 405. At step 1110, the vehicle 102 captures a first plurality of sensor capture datasets via a first sensor coupled to the vehicle over the course of the calibration time period by capturing at least one of the first plurality of sensor capture datasets while the vehicle is at each of the plurality of vehicle positions. At step 1115, the vehicle 102 captures a second plurality of sensor capture datasets via a second sensor coupled to the vehicle over the course of the calibration time period by capturing at least one of the first plurality of sensor capture datasets while the vehicle is at each of the plurality of vehicle positions. Either of steps 1110 and 1115 can occur first, or they can occur at least partially in parallel.

At step 1120, the internal computing system 110 of the vehicle 102 receives the first plurality of sensor capture datasets from the first sensor and the second plurality of sensor capture datasets from the second sensor. At step 1125, the internal computing system 110 of the vehicle 102 identifies, in the first plurality of sensor capture datasets, representations of a first plurality of sensor target features, the first plurality of sensor target features detectable by the first sensor due to a type of the first plurality of sensor target features being detectable by sensors of a type of the first sensor. At step 1130, the internal computing system 110 of the vehicle 102 identifies, in the second plurality of sensor capture datasets, representations of a second plurality of sensor target features, the second plurality of sensor target features detectable by the second sensor due to a type of the second plurality of sensor target features being detectable by sensors of a type of the second sensor. Either of steps 1125 and 1130 can occur first, or they can occur at least partially in parallel.

The first plurality of sensor target features and the second plurality of sensor target features may be on the same targets; for example, if the first sensor is a camera, and the second sensor is a LIDAR sensor, and plurality of sensor targets are the combined extrinsic calibration targets 250 of FIGS. 2E and 5, then the first plurality of sensor target features may be the visual markings (rings) 230 detectable by the camera, while the second plurality of sensor target features are the apertures 225 detectable by the LIDAR. Alternately, the plurality of sensor target features may be on different targets; for example, the first sensor may be a radar sensor and the first plurality of sensor target features may be the trihedral radar calibration targets 220, while the second sensor may be any other sensor (e.g., camera, lidar) and the second plurality of sensor target features may be, for example, the visual markings (rings) 230 or apertures 225 of the combined extrinsic calibration targets 250, or a pattern 210 of a camera intrinsic target such as the targets 200A-C, or any other target described herein.

At step 1135, the internal computing system 110 of the vehicle 102 compares the relative positioning of the representations of the first plurality of sensor target features and the representations of the second plurality of sensor target features to known relative positioning of the first plurality of sensor target features and the second plurality of sensor target features. In some cases, the relative positioning may be determined based on comparison of a position of a particular point in one representation, such as the center, to a particular point in the another representation to which it is being compared, such as the center. Points that can be used instead of the center may include or the highest point, lowest point, leftmost point, rightmost point, a point that is centered along one axis but not another, a point at the widest portion of the representation, a point at the narrowest portion of the representation, a point at a particular edge or vertex, or some combination thereof. At step 1140, the internal computing system 110 of the vehicle 102 generates a transformation based on the comparison, such that the transformation aligns a first location identified by the first sensor and a second location identified by the second sensor.

As a first example, the first sensor may be a camera and the second sensor may be a LIDAR sensor, and the first plurality of sensor target features and the second plurality of sensor target features may both be features of the combined extrinsic calibration targets 250 of FIGS. 2E and 5 such that the first plurality of sensor target features are the visual markings (rings) 230 detectable by the camera and the second plurality of sensor target features are the apertures 225 detectable by the LIDAR. In such a case, the internal computing system 110 of the vehicle 102 identifies a center of a particular aperture 225 based on the LIDAR data, and identifies a center of a ring 230 based on the camera data, compares these at step 1135 and identifies a relative distance between the two locations based on the internal computing system 110's current geographic understanding of the calibration environment. Because the internal computing system 110 understands that these two points should represent the same location in the real world (i.e., their relative positioning indicates no distance between them), the internal computing system 110 generates a transformation—which may include, for example, a translation along X, Y, and/or Z dimensions, a rotation along any axis, a warp or distortion filter, or some combination thereof—that aligns these location points. That is, the transformation translates (1) a mapping of a point from the one sensor's capture data set to a real world position into (2) a mapping of a point from the other sensor's capture data set to the same real world position. While, with just a pair or two of such points, there may be multiple possible transformations that can perform this alignment, the internal computing system 110 can generate a transformation that works consistently for an increasing number of pairs such sets of points—for example, for each aperture 225 and ring 230 combinations of a target 250, and for each target 250 in the calibration environment. As the number of pairs increases, the number of possible transformations that can successfully align these. Different sensors may map the world around them differently; for example, if the camera includes a wide-angle lens while the other sensor (e.g., LIDAR) does not include an analogous distortion effect, the transformation may include some radial movement or other compensation for distortion.

As a second example, the first sensor may be a radar sensor and the second sensor may be a LIDAR sensor, and the first plurality of sensor target features may be trihedral radar calibration targets 220 while the second plurality of sensor target features may be apertures 225 of a combined target 250 or the planar boundaries of a substrate 205 of a camera target 200, each of which is a known distance away from the nearest trihedral radar calibration targets 220. In such a case, the internal computing system 110 of the vehicle 102 identifies a location of the trihedral radar calibration targets 220 based on radar sensor data and a location of the LIDAR target feature based on LIDAR sensor data, compares these at step 1135 and identifies a relative distance between the two locations based on the internal computing system 110's current geographic understanding of the calibration environment. Because the internal computing system 110 understands that these two points should be a known distance away in a particular direction at a particular angle in the real world, the internal computing system 110 generates a transformation—which may include, for example, a translation along X, Y, and/or Z dimensions, a rotation along any axis, a warp or distortion filter, or some combination thereof—that aligns these location points to match the same known distance away in the particular direction at the particular angle as in the real world. While initially there may be multiple possible transformation that can perform this, the internal computing system 110 can generate a transformation that works consistently for multiple such sets of points—for example, for each trihedral radar calibration target 220 and each nearby LIDAR target feature pair in the calibration environment.

At step 1145, the internal computing system 110 of the vehicle 102 receives, from the first sensor and second sensor, post-calibration sensor capture datasets captured by the first sensor and second sensor after the calibration time period. At step 1150, the internal computing system 110 of the vehicle 102 applies the transformation generated in step 1140 to one or both of the post-calibration sensor capture datasets. For example, a representation of a particular object can be identified in a post-calibration sensor capture dataset captured by one sensor after calibration, and the transformation can be applied to find the same object within another post-calibration sensor capture dataset captured by another sensor after calibration. A real-world position of the same object may be found relative to the vehicle 102 based on intrinsic calibration of at least one of the two sensors and/or based on the transformation. In some cases, a representation of an entire space—that is, a three-dimensional volume—in one post-calibration sensor capture dataset captured by one sensor after calibration may then be identified in another post-calibration sensor capture dataset captured by another sensor by applying the transformation to multiple points within the space. Important points, such as vertices (e.g., corners of a room), edges (e.g., edges of a room), or other features may be selected as at least some of these points. With two aligned representations of a 3-D space, objects can be identified around the vehicle that might not otherwise be. For example, a pedestrian wearing all black might not visually stand out against (e.g., contrast against) a background of an asphalt road at night, but a RADAR or LIDAR might easily identify the pedestrian, and the transformation will still allow the computer 110 of the vehicle 102 to understand where that pedestrian is in its camera footage, allowing the vehicle to pay close attention to visual cues from the pedestrian that the RADAR or LIDAR might not catch or understand, such as presence or lack of a pet or small child accompanying the pedestrian. Developing the vehicle's understanding of its surroundings by aligning real-world (and relative) mappings of the inputs it receives from its sensors can save lives in the field of autonomous vehicles by allowing the best aspects of multiple sensors to complement one another to develop a comprehensive view of the vehicle's surroundings. No sensor is perfect at detecting everything—range sensors can see range/depth but not color or brightness, and can have trouble seeing small or fast-moving objects—while cameras can see color and brightness and visual features but can have trouble with depth perception. Thus, each sensor has its strengths, and the alignment made possible by the extrinsic calibration processes discussed in FIG. 11 can allow the best aspects of each sensor (the “pros” of each sensor) to complement each other and compensate for the downsides of each sensor (the “cons” of each sensor). Note that capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. While an initial transformation is generated at step 1140, the vehicle 112 may continue to rotate, and its sensors 180 may continue to capture more sensor data, hence the dashed lines extending back up to steps 1105 and 1110 and 1115 from step 1140. When step 1140 is reached a second, third, or N^(th) time (where N is any integer over 1), the transformation generated the first time step 1140 was reached may be updated, revised, and/or re-generated based on the newly captured sensor data when step 1140 is reached again. Thus, the transformation becomes more accurate as calibration continues.

For some additional context on extrinsic calibration, all sensors' extrinsic properties may include relative positions in X, Y, and/or Z dimensions, as well as roll, pitch, and/or yaw. Target and vehicle locations are ground truthed via the mapping system discussed in step 910 and further as discussed with respect to the transformation of step 1030 of FIG. 10. Sensors of the vehicle 102 and scene surveying system 610 record the scene and each target is detected using a target detection method specific to that sensor and target pair. The measured target location is compared against the mapped target location to derive the total extrinsic sensor error: Extr_(sensor)(R,t)=Σ_(target) ∥RC _(target) +t−D _(target)∥²

Where C_(target) is the measured location of the target and D_(target) is the mapped location of the target. We can collect the intrinsic sensor calibration data (as in FIG. 10) in a similar way, at each frame of recorded data the targets are detected and intrinsics are collected. These intrinsic sensor calibration data (as in FIG. 10) might be the measured distance between pixel coordinates and the lines on a target, or lidar point coordinates and detected planar sides of a target. The total error for a single sensor can be summarized as: ExtrIntr(R,t,α)_(sensor)=Intr_(sensor)(α)+γ_(sensor)Extr_(sensor)(R,t)

The weight γ_(sensor) determines the contribution of that sensor's extrinsic parameter. By collecting the ExtrIntr for every sensor we define a global cost function that describes all intrinsics and extrinsics in the system. We can minimize the total expected error by toggling the calibration parameters for each sensor [R,t,α] via a convex optimization algorithm. The output of the sensor extrinsic calibrations may be a pair of rotation and translation matrices on a per sensor basis with respect to the origin of the 3D space (e.g., as identified via LIDAR).

After the calibration parameters are solved for, tests for the numerical sensitivity of the solution can be performed. This may include, for example, verifying the Jacobian of the solution is near zero in all directions and that the covariance of each parameter is reasonably small (e.g., below a threshold). More sophisticated routines that test for sensitivity to targets and constraints may also be performed.

FIG. 12 is a flow diagram illustrating operations for interactions between the vehicle and the turntable.

At optional step 1205, the turntable 405, vehicle 102, or surveying system 610 identifies that the vehicle 102 is positioned on the platform of the motorized turntable. This may be identified using pressure sensors 735 of the turntable 405, a GNSS or triangulation-based positioning receiver of the vehicle 102 compared to a known location of the turntable 405, image/lidar data captured by the surveying system 610 indicating that the vehicle 102 is positioned on motorized turntable 405, or some combination thereof. In some cases, the pressure sensors 735 may be positioned under or beside the guide railing, for example close behind the “stop” wall or incline, to ensure that the vehicle will apply pressure to them. In other cases, the entire turntable is receptive as a pressure sensor. In any case, this information is communicated to the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405, either within the turntable itself (if via sensors 735) or via the relevant transceiver(s) of FIG. 7. In some cases, either sensor data capture by the sensors of the vehicle 102 or rotation of the platform 420 of the motorized turntable 405 may automatically begin once the pressure sensors identify that the vehicle 102 is on the platform 420 and/or once sensors identify that the vehicle 102 has stopped moving (e.g., IMU of the vehicle 102, regional pressure sensors of regions of the turntable platform 420 surface, scene surveying system 610 camera, or some combination thereof). Rotation of the platform 420 about the base 425 may occur first before sensor data capture if, for example, calibration is previously designated to start with the vehicle 102 rotated to a particular rotation orientation or rotation position that is not the same as the rotation orientation or rotation position that the vehicle 102 is in when it drives (or is otherwise placed) onto the platform 420.

In some cases, the rotation of the platform 420 of the turntable 405 about the base 425 via the motors 730 can manually be triggered instead of being based on, and automatically triggered by, detection of the vehicle at step 1205, for example via an input received at the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405 from a wired or wireless interface that itself receives an input from a human being, the wired or wireless interface being for example a keyboard or touchscreen or mouse or remote control communicating in a wired or wireless fashion with the dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405.

At step 1210, one or more motors 730 of the motorized turntable 405 are activated to rotate the platform 420 of the motorized turntable 405 about the base 425 of the motorized turntable 405 (and therefore vehicle 102 on top of the platform 420 as well) from a first rotation orientation to a second rotation orientation in response to detection that the vehicle is on the turntable. The one or more motors 730 may be deactivated, causing the platform of the motorized turntable 405 (and therefore vehicle 102 on top of the platform 420 as well) to stop rotating about the base 425 with the platform 420 in the second orientation at the stop of rotation. The term “rotation orientation” may be used to refer to an angle, or angular orientation, or angular position. Other terms may be used in place of the term “rotation position,” such as “angle,” “angular position,” “angular orientation,” “position,” or “orientation.” The first rotation orientation and the second rotation orientation may be a predetermined angle away from each other, for example N degrees, where N is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or some amount in between any two of these numbers. The first rotation orientation and the second rotation orientation may be an angle away from each other that is determined by the internal computing system 110 of the vehicle 102, or by the dynamic scene control bridge 710, or by the computing device 745 of the turntable system 405, or by some combination thereof, based on which angle would likely be most efficient, comprehensive, or an optimal balance thereof, in completing calibration of the entire fields of view (FOV) of the sensors 180 of the vehicle 102.

At step 1215, the vehicle 102 uses its IMU (or other rotation detection device) to check whether the vehicle 102 (and therefore the platform 420) is still rotating. As the IMU is a collection of accelerometers and/or gyroscopes and/or other motion or rotation detection devices, the vehicle 102 can alternately separately use accelerometers and/or gyroscopes and/or other motion or rotation detection devices that are among the vehicle 102's sensors 180 to determine this. Alternately, the turntable 405 may use one or more motion sensors of its own (e.g., accelerometer, gyroscope, IMU, or any other motion sensor discussed herein) to identify whether the platform 420 of the turntable 405 is still rotating about the base 425. Alternately still, the scene surveying system 610 may use one or more cameras to visually identify whether the platform of the turntable 405 and/or the vehicle 102 is still rotating. In some cases, the device that detects that the vehicle 102 and/or the platform 420 of the turntable 405 has stopped rotating relative to the base 425 (the vehicle computing system 110, the computing device 745 of the turntable 405, the scene surveying system 610, and/or the dynamic scene control bridge 710) can send a signal identifying the detected stop in rotation to any of the vehicle computing system 110, the computing device 745 of the turntable, the scene surveying system 610, or the dynamic scene control bridge 710.

If, at step 1220, the vehicle 102 or turntable 405 or scene surveying system 610 determines that the rotation has stopped, step 1225 follows step 1220. Otherwise, step 1215 follows step 1220.

In addition, we may use the vehicle 102's other sensors 180, such as one or more cameras, radar sensors, lidar sensors, sonar sensors, and/or sodar sensors instead of or in addition to the IMU, accelerometers, gyroscopes, and/or motion/rotation detection devices to identify when the vehicle 102 (and thus the platform 420) is still rotating relative to the base 425 or not. With all of these sensors, rotation may be identified based on whether regions of the calibration environment that should be motionless—walls, the floor, the ceiling, targets that have not been configured and/or commanded to move, light sources 620, the scene surveying system 610—are changing location between sensor captures (indicating that the vehicle is rotating and/or in motion) or are stationary between sensor captures (indicating that the vehicle is stationary).

At step 1225, the vehicle captures sensor data using one or more of its sensors while the vehicle 102 is at the second position. If, at step 1230, the internal computing device 110 of the vehicle 102 determines that it has finished capturing sensor data while vehicle is at the second rotational orientation/position, then step 1235 follows step 1230, and optionally, the vehicle computing system 110 may send a sensor capture confirmation signal to a computing device associated with the turntable 405, such as dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405. The sensor capture confirmation signal may then be used as a signal that the turntable 405 is allowed to begin (and/or should begin) rotation of the platform 420 about the base 425 from the second rotation orientation to a next rotation orientation. Otherwise, if sensor data capture is not complete step 1225 follows step 1230.

If, at step 1235, the internal computing device 110 of the vehicle 102 determines that sufficient data has been captured by the vehicle 102's sensors 180 to perform calibration—then no more rotations of the platform 420 and the vehicle 102 about the base 425 are needed and step 1240 follows step 1235, thus proceeding from sensor data capture to sensor calibration. Optionally, the vehicle computing system 110 may send a sensor capture completion signal to a computing device associated with the turntable 405, such as dynamic scene control bridge 710 and/or the computing device 745 of the turntable system 405. The sensor capture completion signal may then be used as a signal that the platform 420 of the turntable 405 is allowed to stop (and/or should stop) rotating about the base 425 altogether to allow the vehicle 102 to exit the turntable 405 and the calibration environment, or that the platform 425 of the turntable 405 is allowed to begin (and/or should begin) rotating about the base 425 to an exit orientation that allow the vehicle 102 to exit the turntable and the calibration environment (for example when the calibration environment includes many targets around the turntable 405 except for in an entrance/exit direction, as in FIG. 6 where an optimal entrance/exit direction is on the bottom-right due to lack of targets and obstacles generally in that direction). Otherwise, if the internal computing device 110 of the vehicle 102 does not determine that sufficient data has been captured by the vehicle 102's sensors 180 to perform calibration at step 1235, then step 1210 follows after step 1235, to continue rotations of the platform 420 (and vehicle 102) about the base 425 of the motorized turntable system 405. Depending on the sensors 180 on the vehicle 102 and the data captured by the sensors 180, the sensors 180 may require one or more full 360 degree rotations of the vehicle 102 on the platform 420, or may require less than one full 360 degree rotation of the vehicle 102 on the platform 420. In one embodiment, sufficient data for calibration of a sensor may mean data corresponding to targets covering at least a subset of the complete field of view of a particular sensor (collectively over a number of captures), with the subset reaching and/or exceeding a threshold percentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, 100%).

Some sensors may require more data for calibration than others, and thus, one sensor may have captured sufficient data for calibration while another sensor might not. In such cases, step 1235 may refer to all sensors and thus go through the “NO” arrow if any of the sensors 180 hasn't captured sufficient data. Alternately, a particular sensor capturing sufficient data, or a majority of sensors capturing sufficient data, may be the deciding factor toward “YES” or “NO.” In some cases, step 1235 may refer to each sensor separately, and once a particular sensor has captured sufficient data at step 1235, that sensor may continue on to step 1240 for calibration even if the vehicle 102 on the platform 420 continues to rotate about the base 425 and the remaining sensors continue to capture data. Thus, step 1235 may enable staggered completion of capture of sufficient data for different sensors at different times.

In some cases, sensor data capture and sensor calibration occurs at least partially in parallel; that is, a time period in which sensor data capture occurs may at least partially overlap with a time period in which sensor calibration occurs. In such cases, the sensor may calibrate region by region, for example by calibrating the sensor in one or more regions in which the sensor detects (e.g., “sees”) targets for each data capture until the entire point of view of the sensor, or some sufficient subset is calibrated, with the subset reaching and/or exceeding a threshold percentage (e.g., 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, 100%). Calibration of each sensor may use a different threshold, or certain sensors may share a threshold. When calibration occurs in parallel with capture rather than calibration on the whole sequentially following capture on the whole, step 1235 can simply identify when calibration of one or more sensors has successfully completed, and treat that as a proxy for identifying whether sufficient data is captured by those sensors to perform calibration.

In this case, however, step 1210 now rotates the vehicle 102 from the second position to a third position, and steps 1225 and 1230 refer to the third position. The next time step 1210 is reached in this fashion, it now rotates the vehicle 102 from the third position to a fourth position, and so on. In this way, step 1210 rotates the vehicle 102 on the platform 420 about the base 425 from its current rotational orientation/position to its next rotational orientation/position.

At step 1240, the internal computing device 110 of the vehicle 102 proceeds on from sensor data capture to actual calibration of the sensors, for example as in steps 1025-1045 of FIG. 10 or steps 1125-1150 of FIG. 11. To clarify, as discussed further above, capturing data via the sensors 180 as in steps 1225-1235 and calibrating the sensors 180 as in step 1240 can be performed in parallel, between rotations of the platform 420 and vehicle 102 about the base 425, or in any order that causes the process to be efficient. That is, calibration of data captured by a given one of the sensors 180 can begin immediately after the given sensor captures any new data, and can continue while the vehicle 102 is rotating about the base 425 on the platform 420 of the turntable 405 and while the sensors 180 capture further data. Because calibration and capture may be staggered and/or parallelized so that some further capture occurs after some calibration has started, dashed arrows extend from step 1240 to steps 1210 and 1225.

It should be understood that many of the steps of FIG. 12 (such as 1205, 1215, 1220, 1230, and 1235) are optional.

FIG. 13 is a flow diagram illustrating operations for detection of, and compensation for, a sloped turntable surface.

At optional step 1305, the turntable 405, vehicle 102, or surveying system 610 identifies that the vehicle 102 is positioned on platform 420 of the motorized turntable 405 as in step 1205 of FIG. 12, for example based on pressure sensors in the turntable, positioning receivers of the vehicle 102, and/or on the scene surveying system's visual identification of the vehicle 102's position. Step 1305 (or 1205) may optionally include identifying that the vehicle 102 is specifically in a defined position on the platform 420 such as the defined position identified in FIG. 8B.

At step 1310, the one or more sensors 180 of the vehicle 102 are used to check whether the vehicle 102 is level or on a slope. The vehicle 102 may use an IMU, one or more levels, one or more accelerometers, one or more gyroscopes, one or more lidar sensor, one or more radar sensors, one or more sonar sensors, one or more sodar sensor, one or more cameras, any other sensor discussed herein, or some combination thereof to check whether the vehicle 102 is level or on a slope. For example, the vehicle 102 may use one or more gyroscopes, such as one or more gyroscopes found that are part of an IMU of the vehicle 102, to compare the angle of the vehicle 102 while the vehicle 102 is on the platform 420 to a reference angle of each gyroscope, the reference angle of the gyroscope corresponding to a level slope. The vehicle 102 may alternately or additionally use one or more range sensors, such as one or more lidar, radar, sonar, or sodar sensors, to identify the slope of the vehicle 102 based on range from each range sensor to different points along the platform 420 and/or to different points along a floor that the turntable 420 rests on, where the different points should be equidistant if the vehicle 102 (and thus the turntable 405) is level, or where the different points should have a specific proportional relationship if the vehicle 102 (and thus the turntable 405) is level. The vehicle 102 may alternately or additionally use images from one or more cameras, for example to identify a horizon in the one or more images, to identify a slope of the vehicle 102 (and thus the turntable 405). With cameras, a slope of the floor may be detectable by checking slopes of various edges of the calibration environment that should be parallel or perpendicular in X, Y, or Z dimensions (e.g., edge between floor and wall, edge between one wall and another wall, edge between wall and ceiling) against each other to see if any unexpected angles emerge, which may indicate a slope. Sensors 735 within the turntable 405 itself, such as any of those described above with respect to the vehicle 102 in step 1310, may alternately or additionally be used to detect the slope of the turntable 405 (and thus the vehicle) instead of or alongside the sensors 180 of the vehicle 102.

If, at step 1315, the internal computing device 110 of the vehicle 102 identifies that the vehicle 102 (and therefore the turntable 405) is level while vehicle is at this position, then step 1315 is followed by step 1325; otherwise, step 1315 is followed by step 1320. If the vehicle 102 (and/or turntable 405) uses a gyroscope measurement to determine slope, then the gyroscope angle measurement can be compared to a gyroscope reference angle of the gyroscope, and if a difference between the two is found to exceed an angle threshold (e.g., N degrees, where N is 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10) then the slope is not level. If the vehicle 102 (and/or turntable 405) uses a range sensor to determine slope, then one or more differences from expected range values to different points along the floor exceeding a range threshold (e.g, N millimeters or centimeters, where N is 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10) may indicate that the slope is not level. If the vehicle 102 (and/or turntable 405) uses a camera to determine slope, then differences from expected angles in resulting images that are found to exceed an angle threshold (e.g., N degrees, where N is 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, or 10) may indicate that the slope is not level. At step 1320, the internal computing device 110 of the vehicle 102 (and/or the computing device 745 of the motorized turntable system 405) identifies a specific slope of vehicle 102 (and therefore a slope of the turntable 405) at the vehicle and turntable's current rotation position. At step 1325, the vehicle captures sensor datasets using its sensors 180, optionally at multiple orientations, for example as in steps 910, 1010, 1110, 1115, and/or 1225. In some cases, the slope may have already been identified at step 1310, at which case step 1325 may not entail performing anything. In other cases, the slope may have been identified in a quick, imprecise manner at step 1310, while at step 1325, a more accurate measurement is determined, using any of the sensors of the vehicle 102 and/or of the motorized turntable system 405 discussed with respect to step 1310. In some cases, for example, the motorized turntable 405 may rotate the platform 420 about the base 425 while the vehicle 102 is on the platform 420, and the vehicle 102 may perform additional measurements at different orientations along this rotation and use those additional measurements to more precisely determine slope. These additional slope detection measurements at different orientations may occur at step 1310 and/or at step 1325, and may occur while the platform 420 is stationary relative to the base 425 (for example during pauses in rotation), while the platform 420 is rotating relative to the base 425, or some combination thereof.

At step 1330, the vehicle 102 performs sensor calibration of its sensors, for example as in steps 915-925, 1025-1040, 1125-1150, and/or 1240. The sensor calibration performed at step 1330 may be performed partially (e.g., more sensor calibration capture data is yet to be collected, and/or the motorized turntable still has more rotations to perform) or to completion.

Note that, as discussed with respect to FIG. 12, capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. This may cause calibration and capture to be staggered and/or parallelized so that some further capture occurs after some calibration has started, represented by the dashed arrow from step 1330 to step 1325.

At step 1335, the internal computing device 110 of the vehicle 102 additionally compensates for the slope identified at step 1320 in its calibration of sensors at step 1330. For example, if the turntable is not level surface, but is instead tilted along a north-south axis by one degree, then all of the sensor results will be skewed along the north-south axis. The internal computing device 110 of the vehicle 102 can calculate new positions of everything in the calibration environment by tilting everything by one degree along the north-south axis in the opposite direction as the slope identified in step 1320.

At optional step 1340, the rotation from the current position to the next position may be triggered, much like in step 1210 of FIG. 12. Step 1340 may be triggered when the calibration performed at step 1330 is only partial rather than complete, or when the slope is still not determined to a satisfactory degree (i.e., other angles should be checked). The rotation of step 1340 may be followed by step 1310, detecting slope again after the rotation, which may be helpful since certain slopes may be more detectable while the vehicle 102 is in one orientation versus another orientation due to the fact that vehicles 102 are typically longer along their forward-backward axis than they are wide along their left-right axis and/or simply due to limitations of the sensor(s) used to determine slope at certain orientations that might not be limiting at others. Detection of the slope may be detected, verified, and/or adjusted by the vehicle 102 and/or by the turntable system 405 while the platform 420 is at various orientations with respect to the base 425. Thus, slope detection may occur in parallel with other calibration procedures illustrated in and/or discussed with respect to FIG. 9, FIG. 10, FIG. 11, FIG. 12, and/or FIG. 14. By repeating this slope detection and compensation at a multiple rotation locations, the internal computing device 110 of the vehicle 102 may develop a more nuanced understanding of how level or sloped the vehicle 102, and thus the turntable 405, is at each position.

FIG. 14A is a flow diagram illustrating operations for interactions between the vehicle and a lighting system.

At step 1405, the vehicle 102 captures sensor datasets using its sensors 180, for example as in steps 910, 1010, 1110, 1115, 1225, and/or 1325. At step 1410, the internal computing system 110 of the vehicle 102 identifies whether a characteristic of one or more sensor targets—in this case lighting conditions in at least one area of the calibration environment that includes one or more sensor targets—are suboptimal, at least for the purposes of calibration. In some cases, the computer 110 of the vehicle 102 may identify that a representation of a sensor target that is identified within a sensor dataset (such as a photo or video) captured using the sensor (such as a camera) is suboptimal or not suitable for calibration, for example because the sensor target is too dimly lit, too brightly lit, or lit from the wrong angle (e.g., causing glare, shadows, dimness, brightness, uneven lighting, or otherwise affecting the representation of the sensor target). Such lighting conditions may be suboptimal because they may cause a sensor to not properly or clearly detect out one or more features of the sensor target, such as a checkerboard pattern 210A or ArUco pattern 210B or crosshair pattern 210C of a camera target 200, or a shape 215 of a radar target 220, or a aperture 225/240 and/or marking 230 and/or target ID 235 of a combined camera/depth sensor target 250.

If, at step 1410, the computer 110 of the vehicle 102 determines that the lighting conditions are suboptimal, then step 1410 is followed by step 1415; otherwise, step 1410 is followed by step 1420, at which point the vehicle proceeds from capture to sensor calibration of its sensors, for example as in steps 915-925, 1025-1040, 1125-1150, 1240, and/or 1330.

Note that, as discussed with respect to FIG. 12, capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. This may cause calibration and capture to be staggered and/or parallelized so that some further capture occurs after some calibration has started, represented by the dashed arrow from step 1420 to step 1405.

At step 1415, the internal computing system 110 of the vehicle 102 sends an environment adjustment signal or message to an environment adjustment system (in this case the lighting system 760) to activate one or more actuators 762 and thereby adjust lighting conditions in the at least one area (and affecting one or more sensor targets in the at least one area) of the calibration environment. The one or more actuators 762 may control one or more motors associated with the lighting system 760, one or more switches associated with the lighting system 760, and/or one or more dimmers associated with the lighting system 760. Upon receiving the environment adjustment signal or message from the vehicle 102, the lighting system 760 can activate the one or more actuators 762, and can thereby effect a modification to the characteristic (i.e., the lighting condition) of the one or more sensor targets, for example by brightening one or more light sources 620, by dimming one or more light sources 620, by moving one or more light sources 620 translationally, by rotating one or more light sources 620 (i.e., moving the one or more light sources 620 rotationally), by activating (i.e., turning on) one or more light sources 620, by deactivating (i.e., turning off) one or more light sources 620, by changing a color emitted by (or filtered via color filters applied to) the one or more light sources 620, by otherwise modifying the one or more light sources 620, or some combination thereof. Note that an increase in brightness as discussed herein may refer to brightening one or more light sources 620, activating one or more one or more light sources 620, and/or moving one or more light sources 620. Note that a decrease in brightness as discussed herein may refer to dimming one or more light sources 620, deactivating one or more one or more light sources 620, and/or moving one or more light sources 620.

After step 1415, the process returns to 1405 to capture the sensor data with newly-adjusted (i.e., optimized) lighting. The newly-adjusted lighting is then checked at step 1410 to see whether the adjustment from step 1415 corrected the lighting condition issue identified previously at step 1410 (leading to step 1420), or if further adjustments are required (leading to step 1415 once again).

FIG. 14B is a flow diagram illustrating operations for interactions between the vehicle and a target control system.

At step 1425, the vehicle 102 captures sensor datasets using its sensors 180, for example as in steps 910, 1010, 1110, 1115, 1225, 1325, and/or 1405. At step 1430, the internal computing system 110 of the vehicle 102 identifies whether a characteristic of one or more sensor targets—in this case sensor target positioning of at least one target in the calibration environment is suboptimal, at least for the purposes of calibration. In some cases, the computer 110 of the vehicle 102 may identify that a representation of a sensor target that is identified within a sensor dataset (such as a photo or video or radar image/video or lidar image/video) captured using the sensor (such as a camera or radar or lidar sensor) is suboptimal or not suitable for calibration, for example because the sensor target is located in a position and/or facing an orientation in which the sensor cannot properly or clearly detect out one or more features of the sensor target, such as a checkerboard pattern 210A or ArUco pattern 210B or crosshair pattern 210C of a camera target 200, or a shape 215 of a radar target 220, or a aperture 225/240 and/or marking 230 and/or target ID 235 of a combined camera/depth sensor target 250.

If, at step 1430, the computer 110 of the vehicle 102 determines that the sensor target positioning is sub-optimal, then step 1430 is followed by step 1435; otherwise, step 1430 is followed by step 1440, at which point the vehicle proceeds from capture to sensor calibration of its sensors, for example as in steps 915-925, 1025-1040, 1125-1150, 1240, 1330, and/or 1420.

Note that, as discussed with respect to FIG. 12, capture of data by the sensors 180 of the vehicle 102 may occur in parallel with calibration of the sensors 180 of the vehicle 102. This may cause calibration and capture to be staggered and/or parallelized so that some further capture occurs after some calibration has started, represented by the dashed arrow from step 1440 to step 1425.

At step 1435, the internal computing system 110 of the vehicle 102 sends an environment adjustment signal or message to an environment adjustment system (in this case the target control system 770) to activate one or more actuators 774 and thereby move the at least one sensor target to a more optimal position in the calibration environment. The one or more actuators 774 may control one or more motors associated with the target control system 770 and/or one or more switches associated with the target control system 770. Upon receiving the environment adjustment signal or message from the vehicle 102, the target control system 770 can activate the one or more actuators 774, and can thereby effect a modification to the characteristic (i.e., the positioning) of the one or more sensor targets, for example by activating one or more motors that translationally move one or more targets and/or by activating one or more motors that rotate one or more targets (each about an axis).

After step 1435, the process returns to 1425 to capture the sensor data with newly-moved (i.e., optimized) sensor target positioning. The newly-moved target positioning is then checked at step 1430 to see whether the adjustment from step 1435 corrected the target positioning issue identified previously at step 1430 (leading to step 1440), or if further adjustments are required (leading to step 1435 once again).

In some cases, the adjustment(s) to lighting of FIG. 14A and the adjustment(s) to target positioning of FIG. 14B may both occur following capture of the same sensor dataset with the same sensor, In such cases, the checks of steps 1410 and 1430 may be performed repeatedly, once after each adjustment in either target positioning or lighting, since movement of a sensor target may correct or ameliorate issues with lighting, and on the other hand, adjustment of lighting may also correct or ameliorate issues with target positioning. In such cases, the sending of messages, and the resulting adjustments, of steps 1415 and step 1435, can either be performed sequentially (and then tested at steps 1410 and/or 1430), or can be performed in parallel (and then tested at steps 1410 and/or 1430).

While various flow diagrams provided and described above, such as those in FIGS. 9, 10, 11, 12, 13, 14A, and 14B, may show a particular order of operations performed by some embodiments of the subject technology, it should be understood that such order is exemplary. Alternative embodiments may perform the operations in a different order, combine certain operations, overlap certain operations, or some combination thereof. It should be understood that unless disclosed otherwise, any process illustrated in any flow diagram herein or otherwise illustrated or described herein may be performed by a machine, mechanism, and/or computing system #00 discussed herein, and may be performed automatically (e.g., in response to one or more triggers/conditions described herein), autonomously, semi-autonomously (e.g., based on received instructions), or a combination thereof. Furthermore, any action described herein as occurring in response to one or more particular triggers/conditions should be understood to optionally occur automatically response to the one or more particular triggers/conditions.

As described herein, one aspect of the present technology is the gathering and use of data available from various sources to improve quality and experience. The present disclosure contemplates that in some instances, this gathered data may include personal information. The present disclosure contemplates that the entities involved with such personal information respect and value privacy policies and practices, for example by encrypting such information.

FIG. 15 shows an example of computing system 1500, which can be for example any computing device making up internal computing system 110, remote computing system 150, (potential) passenger device executing rideshare app 170, or any component thereof in which the components of the system are in communication with each other using connection 1505. Connection 1505 can be a physical connection via a bus, or a direct connection into processor 1510, such as in a chipset architecture. Connection 1505 can also be a virtual connection, networked connection, or logical connection.

In some embodiments, computing system 1500 is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices.

Example system 1500 includes at least one processing unit (CPU or processor) 1510 and connection 1505 that couples various system components including system memory 1515, such as read-only memory (ROM) 1520 and random access memory (RAM) 1525 to processor 1510. Computing system 1500 can include a cache of high-speed memory 1512 connected directly with, in close proximity to, or integrated as part of processor 1510.

Processor 1510 can include any general purpose processor and a hardware service or software service, such as services 1532, 1534, and 1536 stored in storage device 1530, configured to control processor 1510 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor 1510 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.

To enable user interaction, computing system 1500 includes an input device 1545, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system 1500 can also include output device 1535, which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system 1500. Computing system 1500 can include communications interface 1540, which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications via wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communications interface 1540 may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system 1500 based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.

Storage device 1530 can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L#), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof.

The storage device 1530 can include software services, servers, services, etc., that when the code that defines such software is executed by the processor 1510, it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor 1510, connection 1505, output device 1535, etc., to carry out the function.

For clarity of explanation, in some instances, the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

Any of the steps, operations, functions, or processes described herein may be performed or implemented by a combination of hardware and software services or services, alone or in combination with other devices. In some embodiments, a service can be software that resides in memory of a client device and/or one or more servers of a content management system and perform one or more functions when a processor executes the software associated with the service. In some embodiments, a service is a program or a collection of programs that carry out a specific function. In some embodiments, a service can be considered a server. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The executable computer instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, solid-state memory devices, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include servers, laptops, smartphones, small form factor personal computers, personal digital assistants, and so on. The functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.

Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. 

What is claimed is:
 1. A system for vehicle sensor calibration, the system comprising: a sensor coupled to a vehicle, wherein the vehicle is rotated into a plurality of orientations by a motorized turntable over a course of a calibration time period, wherein the sensor captures a plurality of sensor capture datasets over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the vehicle is at each of the plurality of orientations; a memory storing instructions; and a processor that executes the instructions, wherein execution of the instructions by the processor causes the processor to: receive the plurality of sensor capture datasets from the sensor, identify, in the plurality of sensor capture datasets, representations of a plurality of sensor targets, calibrate interpretation of data captured by the sensor based on the identified representations of the plurality of sensor targets in the plurality of sensor capture datasets by: identifying a representation of a portion of a calibration environment within the plurality of sensor capture datasets, wherein the calibration environment includes the vehicle and the motorized turntable and the plurality of sensor targets, and generating a transformation based on a mapping of positions corresponding to the representations of the plurality of sensor targets within the representation of the portion of the calibration environment to known positions corresponding to the plurality of sensor targets within the calibration environment, receive a post-calibration sensor capture dataset from the sensor after generating the transformation and while the vehicle is in a second position, identify a representation of an object within a representation of a scene within the post-calibration sensor capture dataset, identify a position of the representation of the object within the representation of the scene, and identify a position of the object relative to the second position of the vehicle by applying the transformation to the position of the representation of the object within the representation of the scene.
 2. The system of claim 1, wherein calibrating the sensor based on the identified representations of the plurality of sensor targets in the plurality of sensor capture datasets comprises: identifying one or more differences between the representations of the plurality of sensor targets and previously stored information about the plurality of sensor targets, generating a correction based on the one or more differences, and applying the correction to the post-calibration sensor capture dataset.
 3. The system of claim 2, wherein the previously stored information about the plurality of sensor targets is from a map of the calibration environment.
 4. The system of claim 2, further comprising a secondary sensor coupled to the vehicle, wherein the previously stored information about the plurality of sensor targets is based on one or more secondary sensor datasets captured by the secondary sensor.
 5. The system of claim 1, further comprising a secondary sensor coupled to the vehicle, wherein the secondary sensor captures a plurality of secondary sensor capture datasets over the course of the calibration time period by capturing at least one of the plurality of secondary sensor capture datasets while the vehicle is at each of the plurality of orientations, wherein the plurality of secondary sensor capture datasets include secondary representations of the plurality of sensor targets.
 6. The system of claim 5, herein calibrating interpretation of data captured by the sensor based on the identified representations of the plurality of sensor targets in the plurality of sensor capture datasets comprises performing an extrinsic calibration of the sensor with respect to the secondary sensor by generating a transformation that maps positions of the representations of the plurality of sensor targets within the plurality of sensor capture datasets to positions of the secondary representations of the plurality of sensor targets within the plurality of secondary sensor capture datasets.
 7. The system of claim 1, wherein each sensor target of the plurality of sensor targets is within a predetermined distance of the motorized turntable.
 8. The system of claim 1, wherein the sensor comprises a camera, and wherein the plurality of sensor targets include a target with a pattern along a surface of the target, wherein the pattern includes at least one of a checkerboard pattern, an ArUco pattern, or a crosshair pattern.
 9. The system of claim 1, wherein the sensor includes a camera, and wherein calibrating interpretation of data captured by the sensor includes correcting distortion caused by a lens of the camera.
 10. The system of claim 1, wherein the sensor comprises a radio distance and ranging (RADAR) sensor, and wherein the plurality of sensor targets include a target having a three-dimensional polyhedral corner shape.
 11. The system of claim 1, wherein the sensor comprises a light distance and ranging (LIDAR) sensor, and wherein the plurality of sensor targets include a target having a surface with at least one aperture.
 12. The system of claim 1, further comprising a wireless communication transceiver, the wireless communication transceiver receiving scene data from a scene surveying device, the scene data captured by the scene surveying device and including a representation of the vehicle and of at least a subset of the plurality of sensor targets, wherein calibrating interpretation of data captured by the sensor is also based on the scene data.
 13. A computer-implemented method for vehicle sensor calibration, the method comprising: receiving a plurality of sensor capture datasets from a sensor coupled to a vehicle, wherein the vehicle is rotated into a plurality of orientations by a motorized turntable over a course of a calibration time period, wherein the sensor captures the plurality of sensor capture datasets over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the vehicle is at each of the plurality of orientations; identifying, in the plurality of sensor capture datasets, representations of a plurality of sensor targets; calibrating interpretation of data captured by the sensor based on the identified representations of the plurality of sensor targets in the plurality of sensor capture datasets by: identifying a representation of a portion of a calibration environment within the plurality of sensor capture datasets, wherein the calibration environment includes the vehicle and the motorized turntable and the plurality of sensor targets, and generating a transformation based on a mapping of positions corresponding to the representations of the plurality of sensor targets within the representation of the portion of the calibration environment to known positions corresponding to the plurality of sensor targets within the calibration environment; receiving a post-calibration sensor capture dataset from the sensor after generating the transformation and while the vehicle is in a second position; identifying a representation of an object within a representation of a scene within the post-calibration sensor capture dataset; identifying a position of the representation of the object within the representation of the scene; and identifying a position of the object relative to the second position of the vehicle by applying the transformation to the position of the representation of the object within the representation of the scene.
 14. The method of claim 13, wherein calibrating interpretation of data captured by the sensor comprises: identifying one or more differences between the representations of the plurality of sensor targets and previously stored information about the plurality of sensor targets, generating a correction based on the one or more differences, and applying the correction to the post-calibration sensor capture dataset.
 15. The method of claim 13, wherein the sensor includes a camera, and wherein calibrating interpretation of data captured by the sensor includes correcting distortion caused by a lens of the camera.
 16. A non-transitory computer readable storage medium having embodied thereon a program, wherein the program is executable by a processor to perform a method of vehicle sensor calibration, the method comprising: receiving a plurality of sensor capture datasets from a sensor coupled to a vehicle, wherein the vehicle is rotated into a plurality of orientations by a motorized turntable over a course of a calibration time period, wherein the sensor captures the plurality of sensor capture datasets over the course of the calibration time period by capturing at least one of the plurality of sensor capture datasets while the vehicle is at each of the plurality of orientations; identifying, in the plurality of sensor capture datasets, representations of a plurality of sensor targets; calibrating interpretation of data captured by the sensor based on the identified representations of the plurality of sensor targets in the plurality of sensor capture datasets by: identifying a representation of a portion of a calibration environment within the plurality of sensor capture datasets, wherein the calibration environment includes the vehicle and the motorized turntable and the plurality of sensor targets, and generating a transformation based on a mapping of positions corresponding to the representations of the plurality of sensor targets within the representation of the portion of the calibration environment to known positions corresponding to the plurality of sensor targets within the calibration environment; receiving a post-calibration sensor capture dataset from the sensor after generating the transformation and while the vehicle is in a second position; identifying a representation of an object within a representation of a scene within the post-calibration sensor capture dataset; identifying a position of the representation of the object within the representation of the scene; and identifying a position of the object relative to the second position of the vehicle by applying the transformation to the position of the representation of the object within the representation of the scene. 